id	sid	tid	token	lemma	pos
fcis-25096	1	1	frontiers	frontier	NOUN
fcis-25096	1	2	in	in	ADP
fcis-25096	1	3	computing	computing	NOUN
fcis-25096	1	4	and	and	CCONJ
fcis-25096	1	5	intelligent	intelligent	ADJ
fcis-25096	1	6	systems	system	NOUN
fcis-25096	1	7	issn	issn	VERB
fcis-25096	1	8	:	:	PUNCT
fcis-25096	1	9	2832	2832	NUM
fcis-25096	1	10	-	-	SYM
fcis-25096	1	11	6024	6024	NUM
fcis-25096	1	12	|	|	NOUN
fcis-25096	1	13	vol	vol	NOUN
fcis-25096	1	14	.	.	PROPN
fcis-25096	2	1	9	9	NUM
fcis-25096	2	2	,	,	PUNCT
fcis-25096	2	3	no	no	INTJ
fcis-25096	2	4	.	.	NOUN
fcis-25096	2	5	2	2	NUM
fcis-25096	2	6	,	,	PUNCT
fcis-25096	2	7	2024	2024	NUM
fcis-25096	2	8	8	8	NUM
fcis-25096	2	9	improved	improve	VERB
fcis-25096	2	10	particle	particle	NOUN
fcis-25096	2	11	swarm	swarm	NOUN
fcis-25096	2	12	optimization	optimization	NOUN
fcis-25096	2	13	algorithm	algorithm	NOUN
fcis-25096	2	14	for	for	ADP
fcis-25096	2	15	mobile	mobile	NOUN
fcis-25096	2	16	robot	robot	NOUN
fcis-25096	2	17	path	path	PROPN
fcis-25096	2	18	planning	planning	PROPN
fcis-25096	2	19	zhiwei	zhiwei	PROPN
fcis-25096	2	20	yang	yang	PROPN
fcis-25096	2	21	xiangyang	xiangyang	PROPN
fcis-25096	2	22	auto	auto	PROPN
fcis-25096	2	23	vocational	vocational	PROPN
fcis-25096	2	24	technical	technical	PROPN
fcis-25096	2	25	college	college	PROPN
fcis-25096	2	26	,	,	PUNCT
fcis-25096	2	27	xiangyang	xiangyang	PROPN
fcis-25096	2	28	hubei	hubei	PROPN
fcis-25096	2	29	,	,	PUNCT
fcis-25096	2	30	china	china	PROPN
fcis-25096	2	31	abstract	abstract	PROPN
fcis-25096	2	32	:	:	PUNCT
fcis-25096	2	33	in	in	ADP
fcis-25096	2	34	solving	solve	VERB
fcis-25096	2	35	the	the	DET
fcis-25096	2	36	path	path	NOUN
fcis-25096	2	37	planning	planning	NOUN
fcis-25096	2	38	problem	problem	NOUN
fcis-25096	2	39	of	of	ADP
fcis-25096	2	40	multi	multi	ADJ
fcis-25096	2	41	-	-	ADJ
fcis-25096	2	42	path	path	ADJ
fcis-25096	2	43	robots	robot	NOUN
fcis-25096	2	44	,	,	PUNCT
fcis-25096	2	45	an	an	DET
fcis-25096	2	46	improved	improved	ADJ
fcis-25096	2	47	particle	particle	NOUN
fcis-25096	2	48	swarm	swarm	NOUN
fcis-25096	2	49	optimization	optimization	NOUN
fcis-25096	2	50	algorithm	algorithm	NOUN
fcis-25096	2	51	is	be	AUX
fcis-25096	2	52	proposed	propose	VERB
fcis-25096	2	53	to	to	PART
fcis-25096	2	54	address	address	VERB
fcis-25096	2	55	the	the	DET
fcis-25096	2	56	drawbacks	drawback	NOUN
fcis-25096	2	57	of	of	ADP
fcis-25096	2	58	premature	premature	ADJ
fcis-25096	2	59	convergence	convergence	NOUN
fcis-25096	2	60	and	and	CCONJ
fcis-25096	2	61	low	low	ADJ
fcis-25096	2	62	search	search	NOUN
fcis-25096	2	63	accuracy	accuracy	NOUN
fcis-25096	2	64	of	of	ADP
fcis-25096	2	65	particle	particle	NOUN
fcis-25096	2	66	swarm	swarm	NOUN
fcis-25096	2	67	optimization	optimization	NOUN
fcis-25096	2	68	algorithm	algorithm	NOUN
fcis-25096	2	69	.	.	PUNCT
fcis-25096	3	1	firstly	firstly	ADV
fcis-25096	3	2	,	,	PUNCT
fcis-25096	3	3	the	the	DET
fcis-25096	3	4	improved	improved	ADJ
fcis-25096	3	5	sine	sine	ADJ
fcis-25096	3	6	chaotic	chaotic	ADJ
fcis-25096	3	7	mapping	mapping	NOUN
fcis-25096	3	8	is	be	AUX
fcis-25096	3	9	used	use	VERB
fcis-25096	3	10	to	to	PART
fcis-25096	3	11	initialize	initialize	VERB
fcis-25096	3	12	the	the	DET
fcis-25096	3	13	population	population	NOUN
fcis-25096	3	14	,	,	PUNCT
fcis-25096	3	15	making	make	VERB
fcis-25096	3	16	it	it	PRON
fcis-25096	3	17	more	more	ADV
fcis-25096	3	18	evenly	evenly	ADV
fcis-25096	3	19	distributed	distribute	VERB
fcis-25096	3	20	in	in	ADP
fcis-25096	3	21	the	the	DET
fcis-25096	3	22	search	search	NOUN
fcis-25096	3	23	space	space	NOUN
fcis-25096	3	24	and	and	CCONJ
fcis-25096	3	25	increasing	increase	VERB
fcis-25096	3	26	population	population	NOUN
fcis-25096	3	27	diversity	diversity	NOUN
fcis-25096	3	28	.	.	PUNCT
fcis-25096	4	1	then	then	ADV
fcis-25096	4	2	,	,	PUNCT
fcis-25096	4	3	the	the	DET
fcis-25096	4	4	concept	concept	NOUN
fcis-25096	4	5	of	of	ADP
fcis-25096	4	6	quantum	quantum	ADJ
fcis-25096	4	7	mechanics	mechanic	NOUN
fcis-25096	4	8	is	be	AUX
fcis-25096	4	9	introduced	introduce	VERB
fcis-25096	4	10	,	,	PUNCT
fcis-25096	4	11	which	which	PRON
fcis-25096	4	12	cancels	cancel	VERB
fcis-25096	4	13	the	the	DET
fcis-25096	4	14	original	original	ADJ
fcis-25096	4	15	particle	particle	NOUN
fcis-25096	4	16	movement	movement	NOUN
fcis-25096	4	17	speed	speed	NOUN
fcis-25096	4	18	and	and	CCONJ
fcis-25096	4	19	sets	set	VERB
fcis-25096	4	20	a	a	DET
fcis-25096	4	21	new	new	ADJ
fcis-25096	4	22	innovative	innovative	ADJ
fcis-25096	4	23	parameter	parameter	NOUN
fcis-25096	4	24	a	a	DET
fcis-25096	4	25	instead	instead	NOUN
fcis-25096	4	26	.	.	PUNCT
fcis-25096	5	1	while	while	SCONJ
fcis-25096	5	2	reducing	reduce	VERB
fcis-25096	5	3	the	the	DET
fcis-25096	5	4	parameters	parameter	NOUN
fcis-25096	5	5	,	,	PUNCT
fcis-25096	5	6	the	the	DET
fcis-25096	5	7	randomness	randomness	NOUN
fcis-25096	5	8	of	of	ADP
fcis-25096	5	9	the	the	DET
fcis-25096	5	10	particles	particle	NOUN
fcis-25096	5	11	is	be	AUX
fcis-25096	5	12	increased	increase	VERB
fcis-25096	5	13	.	.	PUNCT
fcis-25096	6	1	finally	finally	ADV
fcis-25096	6	2	,	,	PUNCT
fcis-25096	6	3	the	the	DET
fcis-25096	6	4	levy	levy	NOUN
fcis-25096	6	5	flight	flight	NOUN
fcis-25096	6	6	strategy	strategy	NOUN
fcis-25096	6	7	is	be	AUX
fcis-25096	6	8	used	use	VERB
fcis-25096	6	9	to	to	PART
fcis-25096	6	10	improve	improve	VERB
fcis-25096	6	11	the	the	DET
fcis-25096	6	12	global	global	ADJ
fcis-25096	6	13	search	search	NOUN
fcis-25096	6	14	ability	ability	NOUN
fcis-25096	6	15	and	and	CCONJ
fcis-25096	6	16	convergence	convergence	NOUN
fcis-25096	6	17	speed	speed	NOUN
fcis-25096	6	18	of	of	ADP
fcis-25096	6	19	the	the	DET
fcis-25096	6	20	algorithm	algorithm	NOUN
fcis-25096	6	21	.	.	PUNCT
fcis-25096	7	1	the	the	DET
fcis-25096	7	2	experimental	experimental	ADJ
fcis-25096	7	3	results	result	NOUN
fcis-25096	7	4	show	show	VERB
fcis-25096	7	5	that	that	SCONJ
fcis-25096	7	6	improving	improve	VERB
fcis-25096	7	7	the	the	DET
fcis-25096	7	8	particle	particle	NOUN
fcis-25096	7	9	swarm	swarm	NOUN
fcis-25096	7	10	optimization	optimization	NOUN
fcis-25096	7	11	algorithm	algorithm	NOUN
fcis-25096	7	12	for	for	ADP
fcis-25096	7	13	path	path	NOUN
fcis-25096	7	14	planning	planning	NOUN
fcis-25096	7	15	enhances	enhance	VERB
fcis-25096	7	16	both	both	CCONJ
fcis-25096	7	17	local	local	ADJ
fcis-25096	7	18	and	and	CCONJ
fcis-25096	7	19	global	global	ADJ
fcis-25096	7	20	search	search	NOUN
fcis-25096	7	21	capabilities	capability	NOUN
fcis-25096	7	22	.	.	PUNCT
fcis-25096	8	1	while	while	SCONJ
fcis-25096	8	2	minimizing	minimize	VERB
fcis-25096	8	3	algorithm	algorithm	NOUN
fcis-25096	8	4	complexity	complexity	NOUN
fcis-25096	8	5	,	,	PUNCT
fcis-25096	8	6	it	it	PRON
fcis-25096	8	7	maximizes	maximize	VERB
fcis-25096	8	8	search	search	NOUN
fcis-25096	8	9	accuracy	accuracy	NOUN
fcis-25096	8	10	and	and	CCONJ
fcis-25096	8	11	plans	plan	VERB
fcis-25096	8	12	the	the	DET
fcis-25096	8	13	shortest	short	ADJ
fcis-25096	8	14	path	path	NOUN
fcis-25096	8	15	that	that	PRON
fcis-25096	8	16	meets	meet	VERB
fcis-25096	8	17	practical	practical	ADJ
fcis-25096	8	18	needs	need	NOUN
fcis-25096	8	19	.	.	PUNCT
fcis-25096	9	1	keywords	keyword	NOUN
fcis-25096	9	2	:	:	PUNCT
fcis-25096	9	3	mobile	mobile	ADJ
fcis-25096	9	4	robot	robot	NOUN
fcis-25096	9	5	;	;	PUNCT
fcis-25096	9	6	path	path	NOUN
fcis-25096	9	7	planning	planning	NOUN
fcis-25096	9	8	;	;	PUNCT
fcis-25096	9	9	particle	particle	NOUN
fcis-25096	9	10	swarm	swarm	NOUN
fcis-25096	9	11	optimization	optimization	NOUN
fcis-25096	9	12	algorithm	algorithm	NOUN
fcis-25096	9	13	;	;	PUNCT
fcis-25096	9	14	sine	sine	VERB
fcis-25096	9	15	chaotic	chaotic	ADJ
fcis-25096	9	16	mapping	mapping	NOUN
fcis-25096	9	17	;	;	PUNCT
fcis-25096	9	18	levy	levy	NOUN
fcis-25096	9	19	flight	flight	NOUN
fcis-25096	9	20	strategy	strategy	NOUN
fcis-25096	9	21	.	.	PUNCT
fcis-25096	10	1	1	1	X
fcis-25096	10	2	.	.	X
fcis-25096	10	3	introduction	introduction	NOUN
fcis-25096	10	4	with	with	ADP
fcis-25096	10	5	the	the	DET
fcis-25096	10	6	rapid	rapid	ADJ
fcis-25096	10	7	development	development	NOUN
fcis-25096	10	8	of	of	ADP
fcis-25096	10	9	modern	modern	ADJ
fcis-25096	10	10	intelligent	intelligent	ADJ
fcis-25096	10	11	unmanned	unmanned	ADJ
fcis-25096	10	12	technology	technology	NOUN
fcis-25096	10	13	and	and	CCONJ
fcis-25096	10	14	the	the	DET
fcis-25096	10	15	gradual	gradual	ADJ
fcis-25096	10	16	maturity	maturity	NOUN
fcis-25096	10	17	of	of	ADP
fcis-25096	10	18	mobile	mobile	ADJ
fcis-25096	10	19	robot	robot	NOUN
fcis-25096	10	20	technology	technology	NOUN
fcis-25096	10	21	,	,	PUNCT
fcis-25096	10	22	it	it	PRON
fcis-25096	10	23	has	have	AUX
fcis-25096	10	24	been	be	AUX
fcis-25096	10	25	widely	widely	ADV
fcis-25096	10	26	applied	apply	VERB
fcis-25096	10	27	in	in	ADP
fcis-25096	10	28	many	many	ADJ
fcis-25096	10	29	industries	industry	NOUN
fcis-25096	10	30	such	such	ADJ
fcis-25096	10	31	as	as	ADP
fcis-25096	10	32	military	military	NOUN
fcis-25096	10	33	,	,	PUNCT
fcis-25096	10	34	transportation	transportation	NOUN
fcis-25096	10	35	,	,	PUNCT
fcis-25096	10	36	logistics	logistic	NOUN
fcis-25096	10	37	,	,	PUNCT
fcis-25096	10	38	and	and	CCONJ
fcis-25096	10	39	industry	industry	NOUN
fcis-25096	10	40	.	.	PUNCT
fcis-25096	11	1	in	in	ADP
fcis-25096	11	2	short	short	ADJ
fcis-25096	11	3	,	,	PUNCT
fcis-25096	11	4	mobile	mobile	ADJ
fcis-25096	11	5	robot	robot	NOUN
fcis-25096	11	6	path	path	NOUN
fcis-25096	11	7	planning	planning	NOUN
fcis-25096	11	8	refers	refer	VERB
fcis-25096	11	9	to	to	ADP
fcis-25096	11	10	finding	find	VERB
fcis-25096	11	11	a	a	DET
fcis-25096	11	12	collision	collision	NOUN
fcis-25096	11	13	free	free	ADJ
fcis-25096	11	14	path	path	NOUN
fcis-25096	11	15	from	from	ADP
fcis-25096	11	16	the	the	DET
fcis-25096	11	17	starting	starting	NOUN
fcis-25096	11	18	point	point	NOUN
fcis-25096	11	19	to	to	ADP
fcis-25096	11	20	the	the	DET
fcis-25096	11	21	target	target	NOUN
fcis-25096	11	22	point	point	NOUN
fcis-25096	11	23	when	when	SCONJ
fcis-25096	11	24	the	the	DET
fcis-25096	11	25	mobile	mobile	ADJ
fcis-25096	11	26	robot	robot	NOUN
fcis-25096	11	27	moves	move	VERB
fcis-25096	11	28	in	in	ADP
fcis-25096	11	29	an	an	DET
fcis-25096	11	30	environment	environment	NOUN
fcis-25096	11	31	with	with	ADP
fcis-25096	11	32	obstacles	obstacle	NOUN
fcis-25096	11	33	without	without	ADP
fcis-25096	11	34	human	human	ADJ
fcis-25096	11	35	intervention	intervention	NOUN
fcis-25096	11	36	.	.	PUNCT
fcis-25096	12	1	path	path	NOUN
fcis-25096	12	2	planning	planning	NOUN
fcis-25096	12	3	technology	technology	NOUN
fcis-25096	12	4	is	be	AUX
fcis-25096	12	5	one	one	NUM
fcis-25096	12	6	of	of	ADP
fcis-25096	12	7	the	the	DET
fcis-25096	12	8	key	key	ADJ
fcis-25096	12	9	technologies	technology	NOUN
fcis-25096	12	10	in	in	ADP
fcis-25096	12	11	the	the	DET
fcis-25096	12	12	field	field	NOUN
fcis-25096	12	13	of	of	ADP
fcis-25096	12	14	intelligent	intelligent	ADJ
fcis-25096	12	15	robot	robot	NOUN
fcis-25096	12	16	research	research	NOUN
fcis-25096	12	17	,	,	PUNCT
fcis-25096	12	18	which	which	PRON
fcis-25096	12	19	to	to	ADP
fcis-25096	12	20	some	some	DET
fcis-25096	12	21	extent	extent	NOUN
fcis-25096	12	22	marks	mark	VERB
fcis-25096	12	23	the	the	DET
fcis-25096	12	24	level	level	NOUN
fcis-25096	12	25	of	of	ADP
fcis-25096	12	26	robot	robot	NOUN
fcis-25096	12	27	intelligence	intelligence	NOUN
fcis-25096	12	28	.	.	PUNCT
fcis-25096	13	1	many	many	ADJ
fcis-25096	13	2	scholars	scholar	NOUN
fcis-25096	13	3	at	at	ADP
fcis-25096	13	4	home	home	ADV
fcis-25096	13	5	and	and	CCONJ
fcis-25096	13	6	abroad	abroad	ADV
fcis-25096	13	7	have	have	AUX
fcis-25096	13	8	conducted	conduct	VERB
fcis-25096	13	9	research	research	NOUN
fcis-25096	13	10	on	on	ADP
fcis-25096	13	11	path	path	NOUN
fcis-25096	13	12	planning	planning	NOUN
fcis-25096	13	13	problems	problem	NOUN
fcis-25096	13	14	for	for	ADP
fcis-25096	13	15	intelligent	intelligent	ADJ
fcis-25096	13	16	robots	robot	NOUN
fcis-25096	13	17	.	.	PUNCT
fcis-25096	14	1	traditional	traditional	ADJ
fcis-25096	14	2	path	path	NOUN
fcis-25096	14	3	planning	planning	NOUN
fcis-25096	14	4	algorithms	algorithm	NOUN
fcis-25096	14	5	can	can	AUX
fcis-25096	14	6	be	be	AUX
fcis-25096	14	7	divided	divide	VERB
fcis-25096	14	8	into	into	ADP
fcis-25096	14	9	search	search	NOUN
fcis-25096	14	10	-	-	PUNCT
fcis-25096	14	11	based	base	VERB
fcis-25096	14	12	path	path	NOUN
fcis-25096	14	13	planning	planning	NOUN
fcis-25096	14	14	and	and	CCONJ
fcis-25096	14	15	sampling	sampling	NOUN
fcis-25096	14	16	-	-	PUNCT
fcis-25096	14	17	based	base	VERB
fcis-25096	14	18	path	path	NOUN
fcis-25096	14	19	planning	planning	NOUN
fcis-25096	14	20	algorithms	algorithm	NOUN
fcis-25096	14	21	.	.	PUNCT
fcis-25096	15	1	search	search	NOUN
fcis-25096	15	2	based	base	VERB
fcis-25096	15	3	algorithms	algorithm	NOUN
fcis-25096	15	4	such	such	ADJ
fcis-25096	15	5	as	as	ADP
fcis-25096	15	6	bfs	bfs	NOUN
fcis-25096	15	7	,	,	PUNCT
fcis-25096	15	8	astar	astar	PROPN
fcis-25096	15	9	algorithm[1	algorithm[1	PROPN
fcis-25096	15	10	]	]	PUNCT
fcis-25096	15	11	,	,	PUNCT
fcis-25096	15	12	dijkstra	dijkstra	ADJ
fcis-25096	15	13	algorithm	algorithm	NOUN
fcis-25096	15	14	,	,	PUNCT
fcis-25096	15	15	etc	etc	X
fcis-25096	15	16	.	.	X
fcis-25096	15	17	have	have	VERB
fcis-25096	15	18	completeness	completeness	NOUN
fcis-25096	15	19	and	and	CCONJ
fcis-25096	15	20	optimality	optimality	NOUN
fcis-25096	15	21	,	,	PUNCT
fcis-25096	15	22	but	but	CCONJ
fcis-25096	15	23	they	they	PRON
fcis-25096	15	24	also	also	ADV
fcis-25096	15	25	have	have	VERB
fcis-25096	15	26	the	the	DET
fcis-25096	15	27	problem	problem	NOUN
fcis-25096	15	28	of	of	ADP
fcis-25096	15	29	falling	fall	VERB
fcis-25096	15	30	into	into	ADP
fcis-25096	15	31	local	local	ADJ
fcis-25096	15	32	optimal	optimal	ADJ
fcis-25096	15	33	traps	trap	NOUN
fcis-25096	15	34	when	when	SCONJ
fcis-25096	15	35	facing	face	VERB
fcis-25096	15	36	complex	complex	ADJ
fcis-25096	15	37	obstacle	obstacle	NOUN
fcis-25096	15	38	environments	environment	NOUN
fcis-25096	15	39	,	,	PUNCT
fcis-25096	15	40	making	make	VERB
fcis-25096	15	41	it	it	PRON
fcis-25096	15	42	difficult	difficult	ADJ
fcis-25096	15	43	to	to	PART
fcis-25096	15	44	generate	generate	VERB
fcis-25096	15	45	optimal	optimal	ADJ
fcis-25096	15	46	paths	path	NOUN
fcis-25096	15	47	.	.	PUNCT
fcis-25096	16	1	sampling	sample	VERB
fcis-25096	16	2	based	base	VERB
fcis-25096	16	3	algorithms	algorithm	NOUN
fcis-25096	16	4	such	such	ADJ
fcis-25096	16	5	as	as	ADP
fcis-25096	16	6	prm	prm	PROPN
fcis-25096	16	7	and	and	CCONJ
fcis-25096	16	8	rrt	rrt	NOUN
fcis-25096	16	9	have	have	VERB
fcis-25096	16	10	probabilistic	probabilistic	ADJ
fcis-25096	16	11	completeness	completeness	NOUN
fcis-25096	16	12	,	,	PUNCT
fcis-25096	16	13	but	but	CCONJ
fcis-25096	16	14	there	there	PRON
fcis-25096	16	15	is	be	VERB
fcis-25096	16	16	also	also	ADV
fcis-25096	16	17	a	a	DET
fcis-25096	16	18	problem	problem	NOUN
fcis-25096	16	19	of	of	ADP
fcis-25096	16	20	generating	generate	VERB
fcis-25096	16	21	routes	route	NOUN
fcis-25096	16	22	that	that	PRON
fcis-25096	16	23	are	be	AUX
fcis-25096	16	24	not	not	PART
fcis-25096	16	25	the	the	DET
fcis-25096	16	26	shortest	short	ADJ
fcis-25096	16	27	or	or	CCONJ
fcis-25096	16	28	optimal	optimal	ADJ
fcis-25096	16	29	.	.	PUNCT
fcis-25096	17	1	in	in	ADP
fcis-25096	17	2	recent	recent	ADJ
fcis-25096	17	3	years	year	NOUN
fcis-25096	17	4	,	,	PUNCT
fcis-25096	17	5	with	with	ADP
fcis-25096	17	6	the	the	DET
fcis-25096	17	7	development	development	NOUN
fcis-25096	17	8	and	and	CCONJ
fcis-25096	17	9	application	application	NOUN
fcis-25096	17	10	of	of	ADP
fcis-25096	17	11	swarm	swarm	NOUN
fcis-25096	17	12	intelligence	intelligence	NOUN
fcis-25096	17	13	algorithms	algorithm	NOUN
fcis-25096	17	14	,	,	PUNCT
fcis-25096	17	15	there	there	PRON
fcis-25096	17	16	have	have	AUX
fcis-25096	17	17	been	be	AUX
fcis-25096	17	18	more	more	ADJ
fcis-25096	17	19	solutions	solution	NOUN
fcis-25096	17	20	to	to	ADP
fcis-25096	17	21	the	the	DET
fcis-25096	17	22	path	path	NOUN
fcis-25096	17	23	planning	planning	NOUN
fcis-25096	17	24	problem	problem	NOUN
fcis-25096	17	25	of	of	ADP
fcis-25096	17	26	mobile	mobile	NOUN
fcis-25096	17	27	robots	robot	NOUN
fcis-25096	17	28	.	.	PUNCT
fcis-25096	18	1	compared	compare	VERB
fcis-25096	18	2	with	with	ADP
fcis-25096	18	3	traditional	traditional	ADJ
fcis-25096	18	4	path	path	NOUN
fcis-25096	18	5	planning	planning	NOUN
fcis-25096	18	6	methods	method	NOUN
fcis-25096	18	7	,	,	PUNCT
fcis-25096	18	8	intelligent	intelligent	ADJ
fcis-25096	18	9	algorithms	algorithm	NOUN
fcis-25096	18	10	make	make	VERB
fcis-25096	18	11	up	up	ADP
fcis-25096	18	12	for	for	ADP
fcis-25096	18	13	many	many	ADJ
fcis-25096	18	14	shortcomings	shortcoming	NOUN
fcis-25096	18	15	of	of	ADP
fcis-25096	18	16	traditional	traditional	ADJ
fcis-25096	18	17	methods	method	NOUN
fcis-25096	18	18	and	and	CCONJ
fcis-25096	18	19	perform	perform	VERB
fcis-25096	18	20	well	well	ADV
fcis-25096	18	21	in	in	ADP
fcis-25096	18	22	solving	solve	VERB
fcis-25096	18	23	complex	complex	ADJ
fcis-25096	18	24	environmental	environmental	ADJ
fcis-25096	18	25	path	path	NOUN
fcis-25096	18	26	planning	planning	NOUN
fcis-25096	18	27	problems	problem	NOUN
fcis-25096	18	28	.	.	PUNCT
fcis-25096	19	1	pso	pso	PROPN
fcis-25096	19	2	was	be	AUX
fcis-25096	19	3	proposed	propose	VERB
fcis-25096	19	4	by	by	ADP
fcis-25096	19	5	kennedy	kennedy	PROPN
fcis-25096	19	6	and	and	CCONJ
fcis-25096	19	7	eberhart	eberhart	NOUN
fcis-25096	19	8	in	in	ADP
fcis-25096	19	9	1995[2	1995[2	NUM
fcis-25096	19	10	]	]	PUNCT
fcis-25096	19	11	.	.	PUNCT
fcis-25096	20	1	in	in	ADP
fcis-25096	20	2	the	the	DET
fcis-25096	20	3	algorithm	algorithm	NOUN
fcis-25096	20	4	,	,	PUNCT
fcis-25096	20	5	candidate	candidate	NOUN
fcis-25096	20	6	solutions	solution	NOUN
fcis-25096	20	7	are	be	AUX
fcis-25096	20	8	treated	treat	VERB
fcis-25096	20	9	as	as	ADP
fcis-25096	20	10	a	a	DET
fcis-25096	20	11	group	group	NOUN
fcis-25096	20	12	of	of	ADP
fcis-25096	20	13	particles	particle	NOUN
fcis-25096	20	14	,	,	PUNCT
fcis-25096	20	15	each	each	PRON
fcis-25096	20	16	with	with	ADP
fcis-25096	20	17	its	its	PRON
fcis-25096	20	18	own	own	ADJ
fcis-25096	20	19	position	position	NOUN
fcis-25096	20	20	and	and	CCONJ
fcis-25096	20	21	velocity	velocity	NOUN
fcis-25096	20	22	,	,	PUNCT
fcis-25096	20	23	and	and	CCONJ
fcis-25096	20	24	the	the	DET
fcis-25096	20	25	optimal	optimal	ADJ
fcis-25096	20	26	solution	solution	NOUN
fcis-25096	20	27	found	find	VERB
fcis-25096	20	28	is	be	AUX
fcis-25096	20	29	recorded	record	VERB
fcis-25096	20	30	.	.	PUNCT
fcis-25096	21	1	particles	particle	NOUN
fcis-25096	21	2	update	update	VERB
fcis-25096	21	3	their	their	PRON
fcis-25096	21	4	position	position	NOUN
fcis-25096	21	5	and	and	CCONJ
fcis-25096	21	6	velocity	velocity	NOUN
fcis-25096	21	7	by	by	ADP
fcis-25096	21	8	following	follow	VERB
fcis-25096	21	9	the	the	DET
fcis-25096	21	10	best	good	ADJ
fcis-25096	21	11	particles	particle	NOUN
fcis-25096	21	12	in	in	ADP
fcis-25096	21	13	the	the	DET
fcis-25096	21	14	group	group	NOUN
fcis-25096	21	15	,	,	PUNCT
fcis-25096	21	16	ultimately	ultimately	ADV
fcis-25096	21	17	finding	find	VERB
fcis-25096	21	18	the	the	DET
fcis-25096	21	19	global	global	ADJ
fcis-25096	21	20	optimal	optimal	ADJ
fcis-25096	21	21	solution	solution	NOUN
fcis-25096	21	22	.	.	PUNCT
fcis-25096	22	1	pso	pso	NOUN
fcis-25096	22	2	has	have	VERB
fcis-25096	22	3	a	a	DET
fcis-25096	22	4	fast	fast	ADJ
fcis-25096	22	5	convergence	convergence	NOUN
fcis-25096	22	6	speed	speed	NOUN
fcis-25096	22	7	and	and	CCONJ
fcis-25096	22	8	strong	strong	ADJ
fcis-25096	22	9	generality	generality	NOUN
fcis-25096	22	10	,	,	PUNCT
fcis-25096	22	11	but	but	CCONJ
fcis-25096	22	12	it	it	PRON
fcis-25096	22	13	also	also	ADV
fcis-25096	22	14	has	have	VERB
fcis-25096	22	15	the	the	DET
fcis-25096	22	16	characteristics	characteristic	NOUN
fcis-25096	22	17	of	of	ADP
fcis-25096	22	18	premature	premature	ADJ
fcis-25096	22	19	convergence	convergence	NOUN
fcis-25096	22	20	,	,	PUNCT
fcis-25096	22	21	low	low	ADJ
fcis-25096	22	22	search	search	NOUN
fcis-25096	22	23	accuracy	accuracy	NOUN
fcis-25096	22	24	,	,	PUNCT
fcis-25096	22	25	and	and	CCONJ
fcis-25096	22	26	low	low	ADJ
fcis-25096	22	27	efficiency	efficiency	NOUN
fcis-25096	22	28	in	in	ADP
fcis-25096	22	29	later	late	ADJ
fcis-25096	22	30	iterations	iteration	NOUN
fcis-25096	22	31	.	.	PUNCT
fcis-25096	23	1	feng	feng	PROPN
fcis-25096	23	2	et	et	PROPN
fcis-25096	23	3	al[3	al[3	NOUN
fcis-25096	23	4	]	]	PUNCT
fcis-25096	23	5	.	.	PUNCT
fcis-25096	24	1	used	use	VERB
fcis-25096	24	2	nonlinear	nonlinear	ADJ
fcis-25096	24	3	inertial	inertial	ADJ
fcis-25096	24	4	weights	weight	NOUN
fcis-25096	24	5	to	to	PART
fcis-25096	24	6	balance	balance	VERB
fcis-25096	24	7	exploration	exploration	NOUN
fcis-25096	24	8	and	and	CCONJ
fcis-25096	24	9	exploitation	exploitation	NOUN
fcis-25096	24	10	,	,	PUNCT
fcis-25096	24	11	and	and	CCONJ
fcis-25096	24	12	improved	improve	VERB
fcis-25096	24	13	the	the	DET
fcis-25096	24	14	convergence	convergence	NOUN
fcis-25096	24	15	speed	speed	NOUN
fcis-25096	24	16	and	and	CCONJ
fcis-25096	24	17	resolution	resolution	NOUN
fcis-25096	24	18	quality	quality	NOUN
fcis-25096	24	19	of	of	ADP
fcis-25096	24	20	the	the	DET
fcis-25096	24	21	algorithm	algorithm	NOUN
fcis-25096	24	22	by	by	ADP
fcis-25096	24	23	dynamically	dynamically	ADV
fcis-25096	24	24	adjusting	adjust	VERB
fcis-25096	24	25	the	the	DET
fcis-25096	24	26	inertial	inertial	ADJ
fcis-25096	24	27	weights	weight	NOUN
fcis-25096	24	28	.	.	PUNCT
fcis-25096	25	1	tian	tian	ADJ
fcis-25096	25	2	et	et	NOUN
fcis-25096	25	3	al[4	al[4	PROPN
fcis-25096	25	4	]	]	PUNCT
fcis-25096	25	5	.	.	PUNCT
fcis-25096	26	1	improved	improve	VERB
fcis-25096	26	2	the	the	DET
fcis-25096	26	3	acceleration	acceleration	NOUN
fcis-25096	26	4	coefficient	coefficient	NOUN
fcis-25096	26	5	of	of	ADP
fcis-25096	26	6	standard	standard	ADJ
fcis-25096	26	7	pso	pso	NOUN
fcis-25096	26	8	to	to	PART
fcis-25096	26	9	make	make	VERB
fcis-25096	26	10	the	the	DET
fcis-25096	26	11	individual	individual	ADJ
fcis-25096	26	12	learning	learn	VERB
fcis-25096	26	13	factor	factor	NOUN
fcis-25096	26	14	and	and	CCONJ
fcis-25096	26	15	social	social	ADJ
fcis-25096	26	16	learning	learning	NOUN
fcis-25096	26	17	factor	factor	NOUN
fcis-25096	26	18	asymmetric	asymmetric	ADJ
fcis-25096	26	19	and	and	CCONJ
fcis-25096	26	20	change	change	NOUN
fcis-25096	26	21	over	over	ADP
fcis-25096	26	22	time	time	NOUN
fcis-25096	26	23	,	,	PUNCT
fcis-25096	26	24	thus	thus	ADV
fcis-25096	26	25	better	well	ADV
fcis-25096	26	26	balancing	balance	VERB
fcis-25096	26	27	exploration	exploration	NOUN
fcis-25096	26	28	and	and	CCONJ
fcis-25096	26	29	exploitation	exploitation	NOUN
fcis-25096	26	30	,	,	PUNCT
fcis-25096	26	31	improving	improve	VERB
fcis-25096	26	32	the	the	DET
fcis-25096	26	33	convergence	convergence	NOUN
fcis-25096	26	34	speed	speed	NOUN
fcis-25096	26	35	and	and	CCONJ
fcis-25096	26	36	quality	quality	NOUN
fcis-25096	26	37	of	of	ADP
fcis-25096	26	38	the	the	DET
fcis-25096	26	39	algorithm	algorithm	NOUN
fcis-25096	26	40	.	.	PUNCT
fcis-25096	27	1	although	although	SCONJ
fcis-25096	27	2	the	the	DET
fcis-25096	27	3	improved	improved	ADJ
fcis-25096	27	4	pso	pso	NOUN
fcis-25096	27	5	mentioned	mention	VERB
fcis-25096	27	6	above	above	ADV
fcis-25096	27	7	has	have	AUX
fcis-25096	27	8	shown	show	VERB
fcis-25096	27	9	good	good	ADJ
fcis-25096	27	10	performance	performance	NOUN
fcis-25096	27	11	in	in	ADP
fcis-25096	27	12	solving	solve	VERB
fcis-25096	27	13	path	path	NOUN
fcis-25096	27	14	planning	planning	NOUN
fcis-25096	27	15	problems	problem	NOUN
fcis-25096	27	16	,	,	PUNCT
fcis-25096	27	17	there	there	PRON
fcis-25096	27	18	are	be	VERB
fcis-25096	27	19	also	also	ADV
fcis-25096	27	20	many	many	ADJ
fcis-25096	27	21	parameters	parameter	NOUN
fcis-25096	27	22	that	that	PRON
fcis-25096	27	23	need	need	VERB
fcis-25096	27	24	to	to	PART
fcis-25096	27	25	be	be	AUX
fcis-25096	27	26	set	set	VERB
fcis-25096	27	27	,	,	PUNCT
fcis-25096	27	28	including	include	VERB
fcis-25096	27	29	1c	1c	NOUN
fcis-25096	27	30	,	,	PUNCT
fcis-25096	27	31	2c	2c	NUM
fcis-25096	27	32	,	,	PUNCT
fcis-25096	27	33			X
fcis-25096	27	34	,	,	PUNCT
fcis-25096	27	35	etc	etc	X
fcis-25096	27	36	.	.	X
fcis-25096	27	37	,	,	PUNCT
fcis-25096	27	38	which	which	PRON
fcis-25096	27	39	are	be	AUX
fcis-25096	27	40	not	not	PART
fcis-25096	27	41	conducive	conducive	ADJ
fcis-25096	27	42	to	to	ADP
fcis-25096	27	43	training	train	VERB
fcis-25096	27	44	optimization	optimization	NOUN
fcis-25096	27	45	and	and	CCONJ
fcis-25096	27	46	the	the	DET
fcis-25096	27	47	algorithm	algorithm	NOUN
fcis-25096	27	48	particles	particle	NOUN
fcis-25096	27	49	lack	lack	VERB
fcis-25096	27	50	randomness	randomness	NOUN
fcis-25096	27	51	,	,	PUNCT
fcis-25096	27	52	making	make	VERB
fcis-25096	27	53	it	it	PRON
fcis-25096	27	54	easy	easy	ADJ
fcis-25096	27	55	to	to	PART
fcis-25096	27	56	fall	fall	VERB
fcis-25096	27	57	into	into	ADP
fcis-25096	27	58	local	local	ADJ
fcis-25096	27	59	optimal	optimal	ADJ
fcis-25096	27	60	solutions	solution	NOUN
fcis-25096	27	61	.	.	PUNCT
fcis-25096	28	1	this	this	DET
fcis-25096	28	2	article	article	NOUN
fcis-25096	28	3	proposes	propose	VERB
fcis-25096	28	4	an	an	DET
fcis-25096	28	5	improved	improved	ADJ
fcis-25096	28	6	particle	particle	NOUN
fcis-25096	28	7	swarm	swarm	NOUN
fcis-25096	28	8	optimization	optimization	NOUN
fcis-25096	28	9	algorithm	algorithm	NOUN
fcis-25096	28	10	,	,	PUNCT
fcis-25096	28	11	which	which	PRON
fcis-25096	28	12	introduces	introduce	VERB
fcis-25096	28	13	a	a	DET
fcis-25096	28	14	re	re	X
fcis-25096	28	15	initialization	initialization	NOUN
fcis-25096	28	16	mechanism	mechanism	NOUN
fcis-25096	28	17	based	base	VERB
fcis-25096	28	18	on	on	ADP
fcis-25096	28	19	global	global	ADJ
fcis-25096	28	20	information	information	NOUN
fcis-25096	28	21	feedback	feedback	NOUN
fcis-25096	28	22	to	to	PART
fcis-25096	28	23	address	address	VERB
fcis-25096	28	24	the	the	DET
fcis-25096	28	25	possible	possible	ADJ
fcis-25096	28	26	stagnation	stagnation	NOUN
fcis-25096	28	27	phenomenon	phenomenon	NOUN
fcis-25096	28	28	in	in	ADP
fcis-25096	28	29	pso	pso	NOUN
fcis-25096	28	30	algorithm	algorithm	NOUN
fcis-25096	28	31	.	.	PUNCT
fcis-25096	29	1	on	on	ADP
fcis-25096	29	2	this	this	DET
fcis-25096	29	3	basis	basis	NOUN
fcis-25096	29	4	,	,	PUNCT
fcis-25096	29	5	the	the	DET
fcis-25096	29	6	concept	concept	NOUN
fcis-25096	29	7	of	of	ADP
fcis-25096	29	8	quantum	quantum	ADJ
fcis-25096	29	9	dynamic	dynamic	ADJ
fcis-25096	29	10	kinematics	kinematic	NOUN
fcis-25096	29	11	is	be	AUX
fcis-25096	29	12	introduced	introduce	VERB
fcis-25096	29	13	,	,	PUNCT
fcis-25096	29	14	and	and	CCONJ
fcis-25096	29	15	the	the	DET
fcis-25096	29	16	original	original	ADJ
fcis-25096	29	17	concept	concept	NOUN
fcis-25096	29	18	of	of	ADP
fcis-25096	29	19	particle	particle	NOUN
fcis-25096	29	20	velocity	velocity	NOUN
fcis-25096	29	21	as	as	ADP
fcis-25096	29	22	the	the	DET
fcis-25096	29	23	direction	direction	NOUN
fcis-25096	29	24	of	of	ADP
fcis-25096	29	25	movement	movement	NOUN
fcis-25096	29	26	is	be	AUX
fcis-25096	29	27	eliminated	eliminate	VERB
fcis-25096	29	28	.	.	PUNCT
fcis-25096	30	1	the	the	DET
fcis-25096	30	2	concept	concept	NOUN
fcis-25096	30	3	of	of	ADP
fcis-25096	30	4	potential	potential	ADJ
fcis-25096	30	5	well	well	ADV
fcis-25096	30	6	is	be	AUX
fcis-25096	30	7	adopted	adopt	VERB
fcis-25096	30	8	,	,	PUNCT
fcis-25096	30	9	so	so	SCONJ
fcis-25096	30	10	that	that	SCONJ
fcis-25096	30	11	the	the	DET
fcis-25096	30	12	next	next	ADJ
fcis-25096	30	13	motion	motion	NOUN
fcis-25096	30	14	of	of	ADP
fcis-25096	30	15	particles	particle	NOUN
fcis-25096	30	16	has	have	VERB
fcis-25096	30	17	no	no	DET
fcis-25096	30	18	relationship	relationship	NOUN
fcis-25096	30	19	with	with	ADP
fcis-25096	30	20	their	their	PRON
fcis-25096	30	21	previous	previous	ADJ
fcis-25096	30	22	motion	motion	NOUN
fcis-25096	30	23	,	,	PUNCT
fcis-25096	30	24	greatly	greatly	ADV
fcis-25096	30	25	increasing	increase	VERB
fcis-25096	30	26	the	the	DET
fcis-25096	30	27	randomness	randomness	NOUN
fcis-25096	30	28	of	of	ADP
fcis-25096	30	29	particle	particle	NOUN
fcis-25096	30	30	motion	motion	NOUN
fcis-25096	30	31	.	.	PUNCT
fcis-25096	31	1	finally	finally	ADV
fcis-25096	31	2	,	,	PUNCT
fcis-25096	31	3	path	path	NOUN
fcis-25096	31	4	planning	planning	NOUN
fcis-25096	31	5	simulation	simulation	NOUN
fcis-25096	31	6	experiments	experiment	NOUN
fcis-25096	31	7	were	be	AUX
fcis-25096	31	8	conducted	conduct	VERB
fcis-25096	31	9	in	in	ADP
fcis-25096	31	10	different	different	ADJ
fcis-25096	31	11	grid	grid	NOUN
fcis-25096	31	12	environments	environment	NOUN
fcis-25096	31	13	,	,	PUNCT
fcis-25096	31	14	and	and	CCONJ
fcis-25096	31	15	the	the	DET
fcis-25096	31	16	experimental	experimental	ADJ
fcis-25096	31	17	results	result	NOUN
fcis-25096	31	18	proved	prove	VERB
fcis-25096	31	19	the	the	DET
fcis-25096	31	20	superiority	superiority	NOUN
fcis-25096	31	21	of	of	ADP
fcis-25096	31	22	our	our	PRON
fcis-25096	31	23	algorithm	algorithm	NOUN
fcis-25096	31	24	in	in	ADP
fcis-25096	31	25	path	path	NOUN
fcis-25096	31	26	planning	planning	NOUN
fcis-25096	31	27	problems	problem	NOUN
fcis-25096	31	28	.	.	PUNCT
fcis-25096	32	1	2	2	X
fcis-25096	32	2	.	.	X
fcis-25096	32	3	particle	particle	NOUN
fcis-25096	32	4	swarm	swarm	NOUN
fcis-25096	32	5	optimization	optimization	NOUN
fcis-25096	32	6	the	the	DET
fcis-25096	32	7	pso	pso	NOUN
fcis-25096	32	8	algorithm	algorithm	NOUN
fcis-25096	32	9	first	first	ADV
fcis-25096	32	10	initializes	initialize	VERB
fcis-25096	32	11	a	a	DET
fcis-25096	32	12	group	group	NOUN
fcis-25096	32	13	of	of	ADP
fcis-25096	32	14	particles	particle	NOUN
fcis-25096	32	15	in	in	ADP
fcis-25096	32	16	the	the	DET
fcis-25096	32	17	solvable	solvable	ADJ
fcis-25096	32	18	space	space	NOUN
fcis-25096	32	19	,	,	PUNCT
fcis-25096	32	20	where	where	SCONJ
fcis-25096	32	21	each	each	DET
fcis-25096	32	22	particle	particle	NOUN
fcis-25096	32	23	represents	represent	VERB
fcis-25096	32	24	a	a	DET
fcis-25096	32	25	potential	potential	ADJ
fcis-25096	32	26	optimal	optimal	ADJ
fcis-25096	32	27	solution	solution	NOUN
fcis-25096	32	28	to	to	ADP
fcis-25096	32	29	the	the	DET
fcis-25096	32	30	extreme	extreme	ADJ
fcis-25096	32	31	value	value	NOUN
fcis-25096	32	32	optimization	optimization	NOUN
fcis-25096	32	33	problem	problem	NOUN
fcis-25096	32	34	.	.	PUNCT
fcis-25096	33	1	the	the	DET
fcis-25096	33	2	characteristics	characteristic	NOUN
fcis-25096	33	3	of	of	ADP
fcis-25096	33	4	the	the	DET
fcis-25096	33	5	particle	particle	NOUN
fcis-25096	33	6	are	be	AUX
fcis-25096	33	7	represented	represent	VERB
fcis-25096	33	8	by	by	ADP
fcis-25096	33	9	three	three	NUM
fcis-25096	33	10	indicators	indicator	NOUN
fcis-25096	33	11	:	:	PUNCT
fcis-25096	33	12	position	position	NOUN
fcis-25096	33	13	,	,	PUNCT
fcis-25096	33	14	velocity	velocity	NOUN
fcis-25096	33	15	,	,	PUNCT
fcis-25096	33	16	and	and	CCONJ
fcis-25096	33	17	fitness	fitness	NOUN
fcis-25096	33	18	value	value	NOUN
fcis-25096	33	19	.	.	PUNCT
fcis-25096	34	1	the	the	DET
fcis-25096	34	2	fitness	fitness	NOUN
fcis-25096	34	3	value	value	NOUN
fcis-25096	34	4	is	be	AUX
fcis-25096	34	5	calculated	calculate	VERB
fcis-25096	34	6	by	by	ADP
fcis-25096	34	7	the	the	DET
fcis-25096	34	8	fitness	fitness	NOUN
fcis-25096	34	9	function	function	NOUN
fcis-25096	34	10	,	,	PUNCT
fcis-25096	34	11	and	and	CCONJ
fcis-25096	34	12	the	the	DET
fcis-25096	34	13	quality	quality	NOUN
fcis-25096	34	14	of	of	ADP
fcis-25096	34	15	its	its	PRON
fcis-25096	34	16	value	value	NOUN
fcis-25096	34	17	indicates	indicate	VERB
fcis-25096	34	18	the	the	DET
fcis-25096	34	19	superiority	superiority	NOUN
fcis-25096	34	20	or	or	CCONJ
fcis-25096	34	21	inferiority	inferiority	NOUN
fcis-25096	34	22	of	of	ADP
fcis-25096	34	23	the	the	DET
fcis-25096	34	24	particle	particle	NOUN
fcis-25096	34	25	.	.	PUNCT
fcis-25096	35	1	during	during	ADP
fcis-25096	35	2	each	each	DET
fcis-25096	35	3	iteration	iteration	NOUN
fcis-25096	35	4	,	,	PUNCT
fcis-25096	35	5	particles	particle	NOUN
fcis-25096	35	6	update	update	VERB
fcis-25096	35	7	their	their	PRON
fcis-25096	35	8	velocity	velocity	NOUN
fcis-25096	35	9	and	and	CCONJ
fcis-25096	35	10	9	9	NUM
fcis-25096	35	11	position	position	NOUN
fcis-25096	35	12	through	through	ADP
fcis-25096	35	13	individual	individual	ADJ
fcis-25096	35	14	extremum	extremum	ADJ
fcis-25096	35	15	and	and	CCONJ
fcis-25096	35	16	global	global	ADJ
fcis-25096	35	17	extremum	extremum	NOUN
fcis-25096	35	18	,	,	PUNCT
fcis-25096	35	19	with	with	ADP
fcis-25096	35	20	the	the	DET
fcis-25096	35	21	following	follow	VERB
fcis-25096	35	22	update	update	NOUN
fcis-25096	35	23	formula	formula	NOUN
fcis-25096	35	24	:	:	PUNCT
fcis-25096	35	25			NOUN
fcis-25096	36	1			SYM
fcis-25096	36	2			NOUN
fcis-25096	36	3			SYM
fcis-25096	36	4			NOUN
fcis-25096	36	5			SYM
fcis-25096	36	6			NOUN
fcis-25096	36	7	1	1	NOUN
fcis-25096	36	8	21	21	NUM
fcis-25096	36	9	.	.	PUNCT
fcis-25096	37	1	(	(	PUNCT
fcis-25096	37	2	)	)	PUNCT
fcis-25096	37	3	(	(	PUNCT
fcis-25096	37	4	)	)	PUNCT
fcis-25096	37	5	(	(	PUNCT
fcis-25096	37	6	)	)	PUNCT
fcis-25096	37	7	best(t)in	best(t)in	NOUN
fcis-25096	37	8	in	in	ADV
fcis-25096	37	9	in	in	ADP
fcis-25096	37	10	int	int	PROPN
fcis-25096	37	11	t	t	PROPN
fcis-25096	37	12	c	c	NOUN
fcis-25096	37	13	rand	rand	PROPN
fcis-25096	37	14	pbest	pb	ADJ
fcis-25096	37	15	t	t	X
fcis-25096	38	1	x	x	SYM
fcis-25096	38	2	t	t	PROPN
fcis-25096	38	3	c	c	PROPN
fcis-25096	38	4	rand	rand	NOUN
fcis-25096	38	5	g	g	NOUN
fcis-25096	38	6	x	x	PART
fcis-25096	38	7	t	t	PROPN
fcis-25096	38	8			X
fcis-25096	38	9			NUM
fcis-25096	38	10			ADJ
fcis-25096	38	11			PROPN
fcis-25096	38	12			VERB
fcis-25096	38	13			NOUN
fcis-25096	38	14			PROPN
fcis-25096	38	15			PROPN
fcis-25096	38	16			PROPN
fcis-25096	38	17			PROPN
fcis-25096	38	18			VERB
fcis-25096	38	19			PROPN
fcis-25096	38	20	(	(	PUNCT
fcis-25096	38	21	1	1	NUM
fcis-25096	38	22	)	)	PUNCT
fcis-25096	38	23	(	(	PUNCT
fcis-25096	38	24	1	1	X
fcis-25096	38	25	)	)	PUNCT
fcis-25096	38	26	(	(	PUNCT
fcis-25096	38	27	)	)	PUNCT
fcis-25096	38	28	(	(	PUNCT
fcis-25096	38	29	1)in	1)in	NUM
fcis-25096	38	30	in	in	ADP
fcis-25096	38	31	inx	inx	PROPN
fcis-25096	38	32	t	t	PROPN
fcis-25096	38	33	x	x	PROPN
fcis-25096	38	34	t	t	PROPN
fcis-25096	38	35	t	t	PART
fcis-25096	38	36			PROPN
fcis-25096	38	37			PROPN
fcis-25096	38	38			X
fcis-25096	38	39	(	(	PUNCT
fcis-25096	38	40	2	2	NUM
fcis-25096	38	41	)	)	PUNCT
fcis-25096	38	42	in	in	ADP
fcis-25096	38	43	the	the	DET
fcis-25096	38	44	formula	formula	NOUN
fcis-25096	38	45	,	,	PUNCT
fcis-25096	38	46			ADJ
fcis-25096	38	47	and	and	CCONJ
fcis-25096	38	48	x	x	AUX
fcis-25096	38	49	represent	represent	VERB
fcis-25096	38	50	the	the	DET
fcis-25096	38	51	velocity	velocity	NOUN
fcis-25096	38	52	and	and	CCONJ
fcis-25096	38	53	position	position	NOUN
fcis-25096	38	54	of	of	ADP
fcis-25096	38	55	particles	particle	NOUN
fcis-25096	38	56	;	;	PUNCT
fcis-25096	38	57			X
fcis-25096	38	58	is	be	AUX
fcis-25096	38	59	the	the	DET
fcis-25096	38	60	inertia	inertia	NOUN
fcis-25096	38	61	weight	weight	NOUN
fcis-25096	38	62	;	;	PUNCT
fcis-25096	38	63	1r	1r	NUM
fcis-25096	38	64	and	and	CCONJ
fcis-25096	38	65	2r	2r	NUM
fcis-25096	38	66	are	be	AUX
fcis-25096	38	67	random	random	ADJ
fcis-25096	38	68	numbers	number	NOUN
fcis-25096	38	69	distributed	distribute	VERB
fcis-25096	38	70	between	between	ADP
fcis-25096	38	71	[	[	X
fcis-25096	38	72	0,1	0,1	NUM
fcis-25096	38	73	]	]	PUNCT
fcis-25096	38	74	;	;	PUNCT
fcis-25096	38	75	1c	1c	NUM
fcis-25096	38	76	and	and	CCONJ
fcis-25096	38	77	2c	2c	NOUN
fcis-25096	38	78	are	be	AUX
fcis-25096	38	79	acceleration	acceleration	NOUN
fcis-25096	38	80	factors	factor	NOUN
fcis-25096	38	81	;	;	PUNCT
fcis-25096	38	82	(	(	PUNCT
fcis-25096	38	83	)	)	PUNCT
fcis-25096	38	84	pbest	pbest	NOUN
fcis-25096	38	85	t	t	NOUN
fcis-25096	38	86	represents	represent	VERB
fcis-25096	38	87	the	the	DET
fcis-25096	38	88	current	current	ADJ
fcis-25096	38	89	optimal	optimal	ADJ
fcis-25096	38	90	particle	particle	NOUN
fcis-25096	38	91	position	position	NOUN
fcis-25096	38	92	,	,	PUNCT
fcis-25096	38	93	while	while	SCONJ
fcis-25096	38	94	(	(	PUNCT
fcis-25096	38	95	)	)	PUNCT
fcis-25096	38	96	gbest	gbest	NOUN
fcis-25096	38	97	t	t	PROPN
fcis-25096	38	98	represents	represent	VERB
fcis-25096	38	99	the	the	DET
fcis-25096	38	100	global	global	ADJ
fcis-25096	38	101	optimal	optimal	ADJ
fcis-25096	38	102	particle	particle	NOUN
fcis-25096	38	103	position	position	NOUN
fcis-25096	38	104	;	;	PUNCT
fcis-25096	38	105	d=1,2,	d=1,2,	NOUN
fcis-25096	38	106	…	…	PUNCT
fcis-25096	38	107	,d	,d	PUNCT
fcis-25096	38	108	;	;	PUNCT
fcis-25096	38	109	i=1,2,	i=1,2,	NOUN
fcis-25096	38	110	…	…	SYM
fcis-25096	38	111	,n	,n	PUNCT
fcis-25096	38	112	3	3	NUM
fcis-25096	38	113	.	.	NUM
fcis-25096	38	114	improved	improve	VERB
fcis-25096	38	115	particle	particle	NOUN
fcis-25096	38	116	swarm	swarm	NOUN
fcis-25096	38	117	optimizations	optimization	NOUN
fcis-25096	38	118	the	the	DET
fcis-25096	38	119	main	main	ADJ
fcis-25096	38	120	reasons	reason	NOUN
fcis-25096	38	121	for	for	ADP
fcis-25096	38	122	the	the	DET
fcis-25096	38	123	poor	poor	ADJ
fcis-25096	38	124	search	search	NOUN
fcis-25096	38	125	ability	ability	NOUN
fcis-25096	38	126	of	of	ADP
fcis-25096	38	127	standard	standard	ADJ
fcis-25096	38	128	pso	pso	NOUN
fcis-25096	38	129	and	and	CCONJ
fcis-25096	38	130	the	the	DET
fcis-25096	38	131	tendency	tendency	NOUN
fcis-25096	38	132	to	to	PART
fcis-25096	38	133	fall	fall	VERB
fcis-25096	38	134	into	into	ADP
fcis-25096	38	135	local	local	ADJ
fcis-25096	38	136	optima	optima	NOUN
fcis-25096	38	137	are	be	AUX
fcis-25096	38	138	:	:	PUNCT
fcis-25096	38	139	(	(	PUNCT
fcis-25096	38	140	1	1	X
fcis-25096	38	141	)	)	PUNCT
fcis-25096	38	142	there	there	PRON
fcis-25096	38	143	are	be	VERB
fcis-25096	38	144	many	many	ADJ
fcis-25096	38	145	parameters	parameter	NOUN
fcis-25096	38	146	that	that	PRON
fcis-25096	38	147	need	need	VERB
fcis-25096	38	148	to	to	PART
fcis-25096	38	149	be	be	AUX
fcis-25096	38	150	set	set	VERB
fcis-25096	38	151	,	,	PUNCT
fcis-25096	38	152	such	such	ADJ
fcis-25096	38	153	as	as	ADP
fcis-25096	38	154	acceleration	acceleration	NOUN
fcis-25096	38	155	weight	weight	NOUN
fcis-25096	38	156	,	,	PUNCT
fcis-25096	38	157	inertia	inertia	NOUN
fcis-25096	38	158	weight	weight	NOUN
fcis-25096	38	159	,	,	PUNCT
fcis-25096	38	160	etc	etc	X
fcis-25096	38	161	.	.	X
fcis-25096	38	162	,	,	PUNCT
fcis-25096	38	163	which	which	PRON
fcis-25096	38	164	are	be	AUX
fcis-25096	38	165	not	not	PART
fcis-25096	38	166	conducive	conducive	ADJ
fcis-25096	38	167	to	to	ADP
fcis-25096	38	168	finding	find	VERB
fcis-25096	38	169	the	the	DET
fcis-25096	38	170	optimal	optimal	ADJ
fcis-25096	38	171	parameters	parameter	NOUN
fcis-25096	38	172	of	of	ADP
fcis-25096	38	173	the	the	DET
fcis-25096	38	174	model	model	NOUN
fcis-25096	38	175	to	to	PART
fcis-25096	38	176	be	be	AUX
fcis-25096	38	177	optimized	optimize	VERB
fcis-25096	38	178	.	.	PUNCT
fcis-25096	39	1	(	(	PUNCT
fcis-25096	39	2	2	2	X
fcis-25096	39	3	)	)	PUNCT
fcis-25096	39	4	the	the	DET
fcis-25096	39	5	lack	lack	NOUN
fcis-25096	39	6	of	of	ADP
fcis-25096	39	7	randomness	randomness	NOUN
fcis-25096	39	8	in	in	ADP
fcis-25096	39	9	particle	particle	NOUN
fcis-25096	39	10	position	position	NOUN
fcis-25096	39	11	changes	change	NOUN
fcis-25096	39	12	can	can	AUX
fcis-25096	39	13	easily	easily	ADV
fcis-25096	39	14	lead	lead	VERB
fcis-25096	39	15	to	to	ADP
fcis-25096	39	16	the	the	DET
fcis-25096	39	17	trap	trap	NOUN
fcis-25096	39	18	of	of	ADP
fcis-25096	39	19	local	local	ADJ
fcis-25096	39	20	optima	optima	NOUN
fcis-25096	39	21	.	.	PUNCT
fcis-25096	40	1	in	in	ADP
fcis-25096	40	2	order	order	NOUN
fcis-25096	40	3	to	to	PART
fcis-25096	40	4	solve	solve	VERB
fcis-25096	40	5	the	the	DET
fcis-25096	40	6	above	above	ADJ
fcis-25096	40	7	problems	problem	NOUN
fcis-25096	40	8	,	,	PUNCT
fcis-25096	40	9	this	this	DET
fcis-25096	40	10	article	article	NOUN
fcis-25096	40	11	improves	improve	VERB
fcis-25096	40	12	the	the	DET
fcis-25096	40	13	pso	pso	NOUN
fcis-25096	40	14	algorithm	algorithm	NOUN
fcis-25096	40	15	,	,	PUNCT
fcis-25096	40	16	and	and	CCONJ
fcis-25096	40	17	the	the	DET
fcis-25096	40	18	specific	specific	ADJ
fcis-25096	40	19	measures	measure	NOUN
fcis-25096	40	20	are	be	AUX
fcis-25096	40	21	as	as	SCONJ
fcis-25096	40	22	follows	follow	NOUN
fcis-25096	40	23	.	.	PUNCT
fcis-25096	41	1	3.1	3.1	NUM
fcis-25096	41	2	.	.	NUM
fcis-25096	41	3	improved	improve	VERB
fcis-25096	41	4	sine	sine	ADJ
fcis-25096	41	5	chaotic	chaotic	ADJ
fcis-25096	41	6	mapping	mapping	NOUN
fcis-25096	41	7	the	the	DET
fcis-25096	41	8	initialization	initialization	NOUN
fcis-25096	41	9	of	of	ADP
fcis-25096	41	10	the	the	DET
fcis-25096	41	11	basic	basic	ADJ
fcis-25096	41	12	population	population	NOUN
fcis-25096	41	13	is	be	AUX
fcis-25096	41	14	a	a	DET
fcis-25096	41	15	random	random	ADJ
fcis-25096	41	16	distribution	distribution	NOUN
fcis-25096	41	17	throughout	throughout	ADP
fcis-25096	41	18	the	the	DET
fcis-25096	41	19	entire	entire	ADJ
fcis-25096	41	20	space	space	NOUN
fcis-25096	41	21	,	,	PUNCT
fcis-25096	41	22	which	which	PRON
fcis-25096	41	23	has	have	VERB
fcis-25096	41	24	strong	strong	ADJ
fcis-25096	41	25	randomness	randomness	NOUN
fcis-25096	41	26	and	and	CCONJ
fcis-25096	41	27	uneven	uneven	ADJ
fcis-25096	41	28	distribution	distribution	NOUN
fcis-25096	41	29	,	,	PUNCT
fcis-25096	41	30	which	which	PRON
fcis-25096	41	31	can	can	AUX
fcis-25096	41	32	cause	cause	VERB
fcis-25096	41	33	problems	problem	NOUN
fcis-25096	41	34	such	such	ADJ
fcis-25096	41	35	as	as	ADP
fcis-25096	41	36	insufficient	insufficient	ADJ
fcis-25096	41	37	population	population	NOUN
fcis-25096	41	38	diversity	diversity	NOUN
fcis-25096	41	39	and	and	CCONJ
fcis-25096	41	40	low	low	ADJ
fcis-25096	41	41	search	search	NOUN
fcis-25096	41	42	efficiency	efficiency	NOUN
fcis-25096	41	43	.	.	PUNCT
fcis-25096	42	1	this	this	DET
fcis-25096	42	2	article	article	NOUN
fcis-25096	42	3	uses	use	VERB
fcis-25096	42	4	chaos	chaos	NOUN
fcis-25096	42	5	mapping	mapping	NOUN
fcis-25096	42	6	mechanism	mechanism	NOUN
fcis-25096	42	7	to	to	PART
fcis-25096	42	8	increase	increase	VERB
fcis-25096	42	9	the	the	DET
fcis-25096	42	10	diversity	diversity	NOUN
fcis-25096	42	11	of	of	ADP
fcis-25096	42	12	the	the	DET
fcis-25096	42	13	population	population	NOUN
fcis-25096	42	14	and	and	CCONJ
fcis-25096	42	15	improve	improve	VERB
fcis-25096	42	16	the	the	DET
fcis-25096	42	17	performance	performance	NOUN
fcis-25096	42	18	of	of	ADP
fcis-25096	42	19	the	the	DET
fcis-25096	42	20	algorithm	algorithm	NOUN
fcis-25096	42	21	.	.	PUNCT
fcis-25096	43	1	its	its	PRON
fcis-25096	43	2	nonlinear	nonlinear	ADJ
fcis-25096	43	3	characteristics	characteristic	NOUN
fcis-25096	43	4	and	and	CCONJ
fcis-25096	43	5	periodic	periodic	ADJ
fcis-25096	43	6	properties	property	NOUN
fcis-25096	43	7	enable	enable	VERB
fcis-25096	43	8	it	it	PRON
fcis-25096	43	9	to	to	PART
fcis-25096	43	10	generate	generate	VERB
fcis-25096	43	11	more	more	ADV
fcis-25096	43	12	complex	complex	ADJ
fcis-25096	43	13	and	and	CCONJ
fcis-25096	43	14	random	random	ADJ
fcis-25096	43	15	sequences	sequence	NOUN
fcis-25096	43	16	,	,	PUNCT
fcis-25096	43	17	which	which	PRON
fcis-25096	43	18	helps	help	VERB
fcis-25096	43	19	to	to	PART
fcis-25096	43	20	enhance	enhance	VERB
fcis-25096	43	21	the	the	DET
fcis-25096	43	22	diversity	diversity	NOUN
fcis-25096	43	23	of	of	ADP
fcis-25096	43	24	the	the	DET
fcis-25096	43	25	population	population	NOUN
fcis-25096	43	26	and	and	CCONJ
fcis-25096	43	27	avoid	avoid	VERB
fcis-25096	43	28	falling	fall	VERB
fcis-25096	43	29	into	into	ADP
fcis-25096	43	30	the	the	DET
fcis-25096	43	31	situation	situation	NOUN
fcis-25096	43	32	of	of	ADP
fcis-25096	43	33	local	local	ADJ
fcis-25096	43	34	optimal	optimal	ADJ
fcis-25096	43	35	solutions	solution	NOUN
fcis-25096	43	36	.	.	PUNCT
fcis-25096	44	1	the	the	DET
fcis-25096	44	2	sequence	sequence	NOUN
fcis-25096	44	3	generated	generate	VERB
fcis-25096	44	4	by	by	ADP
fcis-25096	44	5	the	the	DET
fcis-25096	44	6	iteration	iteration	NOUN
fcis-25096	44	7	of	of	ADP
fcis-25096	44	8	traditional	traditional	ADJ
fcis-25096	44	9	one	one	NUM
fcis-25096	44	10	-	-	PUNCT
fcis-25096	44	11	dimensional	dimensional	ADJ
fcis-25096	44	12	sine	sine	ADJ
fcis-25096	44	13	chaotic	chaotic	ADJ
fcis-25096	44	14	mapping	mapping	NOUN
fcis-25096	44	15	is	be	AUX
fcis-25096	44	16	unevenly	unevenly	ADV
fcis-25096	44	17	distributed	distribute	VERB
fcis-25096	44	18	in	in	ADP
fcis-25096	44	19	phase	phase	NOUN
fcis-25096	44	20	space	space	NOUN
fcis-25096	44	21	,	,	PUNCT
fcis-25096	44	22	and	and	CCONJ
fcis-25096	44	23	its	its	PRON
fcis-25096	44	24	parameter	parameter	NOUN
fcis-25096	44	25	space	space	NOUN
fcis-25096	44	26	in	in	ADP
fcis-25096	44	27	chaotic	chaotic	ADJ
fcis-25096	44	28	state	state	NOUN
fcis-25096	44	29	is	be	AUX
fcis-25096	44	30	narrow	narrow	ADJ
fcis-25096	44	31	.	.	PUNCT
fcis-25096	45	1	therefore	therefore	ADV
fcis-25096	45	2	,	,	PUNCT
fcis-25096	45	3	by	by	ADP
fcis-25096	45	4	introducing	introduce	VERB
fcis-25096	45	5	an	an	DET
fcis-25096	45	6	improved	improved	ADJ
fcis-25096	45	7	sine	sine	ADJ
fcis-25096	45	8	chaotic	chaotic	ADJ
fcis-25096	45	9	mapping	mapping	NOUN
fcis-25096	45	10	[	[	X
fcis-25096	45	11	5	5	NUM
fcis-25096	45	12	]	]	PUNCT
fcis-25096	45	13	,	,	PUNCT
fcis-25096	45	14	the	the	DET
fcis-25096	45	15	distribution	distribution	NOUN
fcis-25096	45	16	is	be	AUX
fcis-25096	45	17	more	more	ADV
fcis-25096	45	18	uniform	uniform	ADJ
fcis-25096	45	19	and	and	CCONJ
fcis-25096	45	20	the	the	DET
fcis-25096	45	21	chaotic	chaotic	ADJ
fcis-25096	45	22	effect	effect	NOUN
fcis-25096	45	23	is	be	AUX
fcis-25096	45	24	better	well	ADJ
fcis-25096	45	25	.	.	PUNCT
fcis-25096	46	1	the	the	DET
fcis-25096	46	2	expression	expression	NOUN
fcis-25096	46	3	is	be	AUX
fcis-25096	46	4	as	as	SCONJ
fcis-25096	46	5	follows	follow	VERB
fcis-25096	46	6	:	:	PUNCT
fcis-25096	46	7	1	1	NUM
fcis-25096	46	8	1	1	NUM
fcis-25096	46	9	1	1	NUM
fcis-25096	46	10	1	1	NUM
fcis-25096	46	11	1	1	NUM
fcis-25096	46	12	sin	sin	NOUN
fcis-25096	46	13	(	(	PUNCT
fcis-25096	46	14	d	d	NOUN
fcis-25096	46	15	)	)	PUNCT
fcis-25096	46	16	sin	sin	NOUN
fcis-25096	46	17	(	(	PUNCT
fcis-25096	46	18	)	)	PUNCT
fcis-25096	46	19	mod1	mod1	NOUN
fcis-25096	47	1	i	i	PRON
fcis-25096	48	1	i	i	PRON
fcis-25096	49	1	i	i	PRON
fcis-25096	50	1	i	i	PRON
fcis-25096	51	1	i	i	PRON
fcis-25096	52	1	i	i	PRON
fcis-25096	52	2	i	i	VERB
fcis-25096	52	3	d	d	X
fcis-25096	52	4	e	e	X
fcis-25096	52	5	e	e	X
fcis-25096	52	6	w	w	PROPN
fcis-25096	52	7	d	d	NOUN
fcis-25096	52	8	e	e	NOUN
fcis-25096	52	9			NOUN
fcis-25096	52	10			NOUN
fcis-25096	52	11			ADV
fcis-25096	52	12			PROPN
fcis-25096	52	13			PROPN
fcis-25096	52	14			VERB
fcis-25096	52	15			PUNCT
fcis-25096	52	16			NOUN
fcis-25096	52	17			NUM
fcis-25096	52	18			PROPN
fcis-25096	52	19			NUM
fcis-25096	53	1			PROPN
fcis-25096	53	2			NOUN
fcis-25096	53	3	(	(	PUNCT
fcis-25096	53	4	3	3	X
fcis-25096	53	5	)	)	PUNCT
fcis-25096	53	6	among	among	ADP
fcis-25096	53	7	them	they	PRON
fcis-25096	53	8	,	,	PUNCT
fcis-25096	53	9	1iw	1iw	ADJ
fcis-25096	53	10			ADV
fcis-25096	53	11	,	,	PUNCT
fcis-25096	53	12	1di	1di	NUM
fcis-25096	53	13	and	and	CCONJ
fcis-25096	53	14	1ie	1ie	PROPN
fcis-25096	53	15			PUNCT
fcis-25096	53	16	are	be	AUX
fcis-25096	53	17	iterative	iterative	ADJ
fcis-25096	53	18	sequence	sequence	NOUN
fcis-25096	53	19	values	value	NOUN
fcis-25096	53	20	,	,	PUNCT
fcis-25096	53	21	i	i	PRON
fcis-25096	53	22	is	be	AUX
fcis-25096	53	23	a	a	DET
fcis-25096	53	24	non	non	ADJ
fcis-25096	53	25	negative	negative	ADJ
fcis-25096	53	26	integer	integer	NOUN
fcis-25096	53	27	,	,	PUNCT
fcis-25096	53	28	0	0	NUM
fcis-25096	54	1	(	(	PUNCT
fcis-25096	54	2	0,1)w	0,1)w	NUM
fcis-25096	54	3			NOUN
fcis-25096	54	4	0	0	NUM
fcis-25096	54	5	;	;	PUNCT
fcis-25096	54	6			NOUN
fcis-25096	54	7	is	be	AUX
fcis-25096	54	8	a	a	DET
fcis-25096	54	9	system	system	NOUN
fcis-25096	54	10	parameter,	parameter,	ADV
fcis-25096	54	11	=	=	NOUN
fcis-25096	54	12	1500	1500	NUM
fcis-25096	54	13	.	.	PUNCT
fcis-25096	55	1	fig	fig	NOUN
fcis-25096	55	2	1	1	NUM
fcis-25096	55	3	.	.	PUNCT
fcis-25096	55	4	comparison	comparison	NOUN
fcis-25096	55	5	of	of	ADP
fcis-25096	55	6	distribution	distribution	NOUN
fcis-25096	55	7	of	of	ADP
fcis-25096	55	8	improved	improved	ADJ
fcis-25096	55	9	sine	sine	NOUN
fcis-25096	55	10	and	and	CCONJ
fcis-25096	55	11	sine	sine	ADJ
fcis-25096	55	12	chaotic	chaotic	ADJ
fcis-25096	55	13	maps	map	NOUN
fcis-25096	55	14	from	from	ADP
fcis-25096	55	15	figure	figure	NOUN
fcis-25096	55	16	(	(	PUNCT
fcis-25096	55	17	1	1	NUM
fcis-25096	55	18	)	)	PUNCT
fcis-25096	55	19	,	,	PUNCT
fcis-25096	55	20	it	it	PRON
fcis-25096	55	21	can	can	AUX
fcis-25096	55	22	be	be	AUX
fcis-25096	55	23	observed	observe	VERB
fcis-25096	55	24	that	that	SCONJ
fcis-25096	55	25	both	both	CCONJ
fcis-25096	55	26	the	the	DET
fcis-25096	55	27	improved	improved	ADJ
fcis-25096	55	28	sine	sine	NOUN
fcis-25096	55	29	and	and	CCONJ
fcis-25096	55	30	sine	sine	ADJ
fcis-25096	55	31	mapping	mapping	NOUN
fcis-25096	55	32	distributions	distribution	NOUN
fcis-25096	55	33	are	be	AUX
fcis-25096	55	34	between	between	ADP
fcis-25096	55	35	[	[	X
fcis-25096	55	36	0,1	0,1	NUM
fcis-25096	55	37	]	]	PUNCT
fcis-25096	55	38	,	,	PUNCT
fcis-25096	55	39	and	and	CCONJ
fcis-25096	55	40	the	the	DET
fcis-25096	55	41	improved	improved	ADJ
fcis-25096	55	42	sine	sine	ADJ
fcis-25096	55	43	chaotic	chaotic	ADJ
fcis-25096	55	44	mapping	mapping	NOUN
fcis-25096	55	45	distribution	distribution	NOUN
fcis-25096	55	46	is	be	AUX
fcis-25096	55	47	more	more	ADV
fcis-25096	55	48	uniform	uniform	ADJ
fcis-25096	55	49	.	.	PUNCT
fcis-25096	56	1	its	its	PRON
fcis-25096	56	2	chaos	chaos	NOUN
fcis-25096	56	3	can	can	AUX
fcis-25096	56	4	replace	replace	VERB
fcis-25096	56	5	random	random	ADJ
fcis-25096	56	6	initialization	initialization	NOUN
fcis-25096	56	7	and	and	CCONJ
fcis-25096	56	8	make	make	VERB
fcis-25096	56	9	the	the	DET
fcis-25096	56	10	population	population	NOUN
fcis-25096	56	11	more	more	ADV
fcis-25096	56	12	evenly	evenly	ADV
fcis-25096	56	13	distributed	distribute	VERB
fcis-25096	56	14	in	in	ADP
fcis-25096	56	15	the	the	DET
fcis-25096	56	16	search	search	NOUN
fcis-25096	56	17	space	space	NOUN
fcis-25096	56	18	.	.	PUNCT
fcis-25096	57	1	3.2	3.2	NUM
fcis-25096	57	2	.	.	X
fcis-25096	58	1	optimizing	optimize	VERB
fcis-25096	58	2	pso	pso	NOUN
fcis-25096	58	3	algorithm	algorithm	NOUN
fcis-25096	58	4	based	base	VERB
fcis-25096	58	5	on	on	ADP
fcis-25096	58	6	quantum	quantum	NOUN
fcis-25096	58	7	computing	computing	NOUN
fcis-25096	58	8	although	although	SCONJ
fcis-25096	58	9	particle	particle	NOUN
fcis-25096	58	10	swarm	swarm	NOUN
fcis-25096	58	11	optimization	optimization	NOUN
fcis-25096	58	12	algorithm	algorithm	NOUN
fcis-25096	58	13	is	be	AUX
fcis-25096	58	14	easy	easy	ADJ
fcis-25096	58	15	to	to	PART
fcis-25096	58	16	use	use	VERB
fcis-25096	58	17	,	,	PUNCT
fcis-25096	58	18	it	it	PRON
fcis-25096	58	19	requires	require	VERB
fcis-25096	58	20	a	a	DET
fcis-25096	58	21	large	large	ADJ
fcis-25096	58	22	number	number	NOUN
fcis-25096	58	23	of	of	ADP
fcis-25096	58	24	parameters	parameter	NOUN
fcis-25096	58	25	to	to	PART
fcis-25096	58	26	be	be	AUX
fcis-25096	58	27	set	set	VERB
fcis-25096	58	28	,	,	PUNCT
fcis-25096	58	29	which	which	PRON
fcis-25096	58	30	is	be	AUX
fcis-25096	58	31	not	not	PART
fcis-25096	58	32	conducive	conducive	ADJ
fcis-25096	58	33	to	to	ADP
fcis-25096	58	34	finding	find	VERB
fcis-25096	58	35	the	the	DET
fcis-25096	58	36	optimal	optimal	ADJ
fcis-25096	58	37	parameters	parameter	NOUN
fcis-25096	58	38	.	.	PUNCT
fcis-25096	59	1	moreover	moreover	ADV
fcis-25096	59	2	,	,	PUNCT
fcis-25096	59	3	the	the	DET
fcis-25096	59	4	changes	change	NOUN
fcis-25096	59	5	in	in	ADP
fcis-25096	59	6	particle	particle	NOUN
fcis-25096	59	7	positions	position	NOUN
fcis-25096	59	8	lack	lack	VERB
fcis-25096	59	9	randomness	randomness	NOUN
fcis-25096	59	10	and	and	CCONJ
fcis-25096	59	11	are	be	AUX
fcis-25096	59	12	prone	prone	ADJ
fcis-25096	59	13	to	to	ADP
fcis-25096	59	14	falling	fall	VERB
fcis-25096	59	15	into	into	ADP
fcis-25096	59	16	the	the	DET
fcis-25096	59	17	trap	trap	NOUN
fcis-25096	59	18	of	of	ADP
fcis-25096	59	19	local	local	ADJ
fcis-25096	59	20	optima	optima	NOUN
fcis-25096	59	21	.	.	PUNCT
fcis-25096	60	1	the	the	DET
fcis-25096	60	2	derivation	derivation	NOUN
fcis-25096	60	3	process	process	NOUN
fcis-25096	60	4	of	of	ADP
fcis-25096	60	5	pso	pso	NOUN
fcis-25096	60	6	algorithm	algorithm	NOUN
fcis-25096	60	7	based	base	VERB
fcis-25096	60	8	on	on	ADP
fcis-25096	60	9	quantum	quantum	NOUN
fcis-25096	60	10	computing	computing	NOUN
fcis-25096	60	11	optimization[6	optimization[6	NOUN
fcis-25096	60	12	]	]	PUNCT
fcis-25096	60	13	is	be	AUX
fcis-25096	60	14	based	base	VERB
fcis-25096	60	15	on	on	ADP
fcis-25096	60	16	quantum	quantum	ADJ
fcis-25096	60	17	dynamics	dynamic	NOUN
fcis-25096	60	18	formula	formula	NOUN
fcis-25096	60	19	,	,	PUNCT
fcis-25096	60	20	and	and	CCONJ
fcis-25096	60	21	the	the	DET
fcis-25096	60	22	specific	specific	ADJ
fcis-25096	60	23	position	position	NOUN
fcis-25096	60	24	of	of	ADP
fcis-25096	60	25	particles	particle	NOUN
fcis-25096	60	26	is	be	AUX
fcis-25096	60	27	determined	determine	VERB
fcis-25096	60	28	through	through	ADP
fcis-25096	60	29	monte	monte	PROPN
fcis-25096	60	30	carlo	carlo	PROPN
fcis-25096	60	31	inverse	inverse	NOUN
fcis-25096	60	32	method	method	NOUN
fcis-25096	60	33	.	.	PUNCT
fcis-25096	61	1	only	only	ADV
fcis-25096	61	2	one	one	NUM
fcis-25096	61	3	parameter	parameter	NOUN
fcis-25096	61	4	needs	need	VERB
fcis-25096	61	5	to	to	PART
fcis-25096	61	6	be	be	AUX
fcis-25096	61	7	adjusted	adjust	VERB
fcis-25096	61	8	to	to	PART
fcis-25096	61	9	update	update	VERB
fcis-25096	61	10	the	the	DET
fcis-25096	61	11	equation	equation	NOUN
fcis-25096	61	12	,	,	PUNCT
fcis-25096	61	13	making	make	VERB
fcis-25096	61	14	the	the	DET
fcis-25096	61	15	implementation	implementation	NOUN
fcis-25096	61	16	and	and	CCONJ
fcis-25096	61	17	parameter	parameter	NOUN
fcis-25096	61	18	selection	selection	NOUN
fcis-25096	61	19	of	of	ADP
fcis-25096	61	20	the	the	DET
fcis-25096	61	21	algorithm	algorithm	NOUN
fcis-25096	61	22	easier	easy	ADJ
fcis-25096	61	23	.	.	PUNCT
fcis-25096	62	1	at	at	ADP
fcis-25096	62	2	the	the	DET
fcis-25096	62	3	same	same	ADJ
fcis-25096	62	4	time	time	NOUN
fcis-25096	62	5	,	,	PUNCT
fcis-25096	62	6	in	in	ADP
fcis-25096	62	7	order	order	NOUN
fcis-25096	62	8	to	to	PART
fcis-25096	62	9	increase	increase	VERB
fcis-25096	62	10	the	the	DET
fcis-25096	62	11	randomness	randomness	NOUN
fcis-25096	62	12	of	of	ADP
fcis-25096	62	13	particle	particle	NOUN
fcis-25096	62	14	position	position	NOUN
fcis-25096	62	15	,	,	PUNCT
fcis-25096	62	16	the	the	DET
fcis-25096	62	17	movement	movement	NOUN
fcis-25096	62	18	direction	direction	NOUN
fcis-25096	62	19	attribute	attribute	NOUN
fcis-25096	62	20	of	of	ADP
fcis-25096	62	21	particles	particle	NOUN
fcis-25096	62	22	is	be	AUX
fcis-25096	62	23	eliminated	eliminate	VERB
fcis-25096	62	24	.	.	PUNCT
fcis-25096	63	1	assuming	assume	VERB
fcis-25096	63	2	the	the	DET
fcis-25096	63	3	population	population	NOUN
fcis-25096	63	4	size	size	NOUN
fcis-25096	63	5	is	be	AUX
fcis-25096	63	6	m	m	PROPN
fcis-25096	63	7	and	and	CCONJ
fcis-25096	63	8	the	the	DET
fcis-25096	63	9	index	index	NOUN
fcis-25096	63	10	i	i	PRON
fcis-25096	63	11	represents	represent	VERB
fcis-25096	63	12	the	the	DET
fcis-25096	63	13	i	i	PROPN
fcis-25096	63	14	-	-	PUNCT
fcis-25096	63	15	th	th	X
fcis-25096	63	16	particle	particle	NOUN
fcis-25096	63	17	,	,	PUNCT
fcis-25096	63	18	the	the	DET
fcis-25096	63	19	average	average	ADJ
fcis-25096	63	20	value	value	NOUN
fcis-25096	63	21	of	of	ADP
fcis-25096	63	22	the	the	DET
fcis-25096	63	23	best	good	ADJ
fcis-25096	63	24	position	position	NOUN
fcis-25096	63	25	of	of	ADP
fcis-25096	63	26	the	the	DET
fcis-25096	63	27	particles	particle	NOUN
fcis-25096	63	28	in	in	ADP
fcis-25096	63	29	the	the	DET
fcis-25096	63	30	current	current	ADJ
fcis-25096	63	31	population	population	NOUN
fcis-25096	63	32	,	,	PUNCT
fcis-25096	63	33	bestm	bestm	NOUN
fcis-25096	63	34	,	,	PUNCT
fcis-25096	63	35	is	be	AUX
fcis-25096	63	36	calculated	calculate	VERB
fcis-25096	63	37	as	as	SCONJ
fcis-25096	63	38	follows	follow	VERB
fcis-25096	63	39	:	:	PUNCT
fcis-25096	63	40	1	1	NUM
fcis-25096	63	41	1	1	NUM
fcis-25096	63	42	m	m	VERB
fcis-25096	63	43	best	good	ADJ
fcis-25096	64	1	i	i	INTJ
fcis-25096	64	2	i	i	PRON
fcis-25096	64	3	m	m	VERB
fcis-25096	64	4	pbest	pb	ADJ
fcis-25096	64	5	m	m	VERB
fcis-25096	64	6			ADJ
fcis-25096	64	7			ADJ
fcis-25096	64	8			X
fcis-25096	64	9	(	(	PUNCT
fcis-25096	64	10	4	4	NUM
fcis-25096	64	11	)	)	PUNCT
fcis-25096	64	12	among	among	ADP
fcis-25096	64	13	them	they	PRON
fcis-25096	64	14	,	,	PUNCT
fcis-25096	64	15	p	p	PRON
fcis-25096	64	16	ibest	ib	ADJ
fcis-25096	64	17	represents	represent	VERB
fcis-25096	64	18	the	the	DET
fcis-25096	64	19	current	current	ADJ
fcis-25096	64	20	optimal	optimal	ADJ
fcis-25096	64	21	position	position	NOUN
fcis-25096	64	22	of	of	ADP
fcis-25096	64	23	the	the	DET
fcis-25096	64	24	i	i	PROPN
fcis-25096	64	25	-	-	PUNCT
fcis-25096	64	26	th	th	X
fcis-25096	64	27	particle	particle	NOUN
fcis-25096	64	28	.	.	PUNCT
fcis-25096	65	1	the	the	DET
fcis-25096	65	2	potential	potential	ADJ
fcis-25096	65	3	field	field	NOUN
fcis-25096	65	4	formula	formula	NOUN
fcis-25096	65	5	for	for	ADP
fcis-25096	65	6	the	the	DET
fcis-25096	65	7	i	i	PROPN
fcis-25096	65	8	-	-	PUNCT
fcis-25096	65	9	th	th	X
fcis-25096	65	10	particle	particle	NOUN
fcis-25096	65	11	is	be	AUX
fcis-25096	65	12	as	as	SCONJ
fcis-25096	65	13	follows	follow	VERB
fcis-25096	65	14	:	:	PUNCT
fcis-25096	65	15	10	10	NUM
fcis-25096	65	16	.	.	PUNCT
fcis-25096	66	1	(	(	PUNCT
fcis-25096	66	2	1	1	X
fcis-25096	66	3	)	)	PUNCT
fcis-25096	66	4	.i	.i	NOUN
fcis-25096	66	5	ip	ip	NOUN
fcis-25096	66	6	pbest	pb	ADJ
fcis-25096	66	7	gbest	gbest	NOUN
fcis-25096	66	8			PROPN
fcis-25096	66	9			ADV
fcis-25096	66	10			PROPN
fcis-25096	66	11	(	(	PUNCT
fcis-25096	66	12	5	5	NUM
fcis-25096	66	13	)	)	PUNCT
fcis-25096	66	14	among	among	ADP
fcis-25096	66	15	them	they	PRON
fcis-25096	66	16	,	,	PUNCT
fcis-25096	66	17			X
fcis-25096	66	18	is	be	AUX
fcis-25096	66	19	a	a	DET
fcis-25096	66	20	random	random	ADJ
fcis-25096	66	21	number	number	NOUN
fcis-25096	66	22	with	with	ADP
fcis-25096	66	23	a	a	DET
fcis-25096	66	24	value	value	NOUN
fcis-25096	66	25	range	range	NOUN
fcis-25096	66	26	of	of	ADP
fcis-25096	66	27	[	[	X
fcis-25096	66	28	0,1	0,1	NUM
fcis-25096	66	29	]	]	PUNCT
fcis-25096	66	30	;	;	PUNCT
fcis-25096	66	31	g	g	PROPN
fcis-25096	66	32	ibest	ib	ADJ
fcis-25096	66	33	represents	represent	VERB
fcis-25096	66	34	the	the	DET
fcis-25096	66	35	globally	globally	ADV
fcis-25096	66	36	optimal	optimal	ADJ
fcis-25096	66	37	particle	particle	NOUN
fcis-25096	66	38	.	.	PUNCT
fcis-25096	67	1	the	the	DET
fcis-25096	67	2	monte	monte	PROPN
fcis-25096	67	3	carlo	carlo	PROPN
fcis-25096	67	4	method	method	NOUN
fcis-25096	67	5	is	be	AUX
fcis-25096	67	6	used	use	VERB
fcis-25096	67	7	to	to	PART
fcis-25096	67	8	update	update	VERB
fcis-25096	67	9	the	the	DET
fcis-25096	67	10	particle	particle	NOUN
fcis-25096	67	11	position	position	NOUN
fcis-25096	67	12	formula	formula	NOUN
fcis-25096	67	13	as	as	SCONJ
fcis-25096	67	14	follows	follow	VERB
fcis-25096	67	15	:	:	PUNCT
fcis-25096	67	16	1	1	NUM
fcis-25096	67	17	ln	ln	NOUN
fcis-25096	67	18	(	(	PUNCT
fcis-25096	67	19	)	)	PUNCT
fcis-25096	67	20	i	i	PRON
fcis-25096	68	1	i	i	PRON
fcis-25096	68	2	ix	ix	ADP
fcis-25096	69	1	p	p	PROPN
fcis-25096	69	2	mbest	mb	ADJ
fcis-25096	69	3	x	x	PROPN
fcis-25096	69	4			NOUN
fcis-25096	69	5			NUM
fcis-25096	70	1			PROPN
fcis-25096	70	2			NOUN
fcis-25096	70	3	(	(	PUNCT
fcis-25096	70	4	6	6	NUM
fcis-25096	70	5	)	)	PUNCT
fcis-25096	70	6	among	among	ADP
fcis-25096	70	7	them	they	PRON
fcis-25096	70	8	,	,	PUNCT
fcis-25096	70	9	the	the	DET
fcis-25096	70	10	value	value	NOUN
fcis-25096	70	11	range	range	NOUN
fcis-25096	70	12	of	of	ADP
fcis-25096	70	13			NOUN
fcis-25096	70	14	is	be	AUX
fcis-25096	70	15	[	[	X
fcis-25096	70	16	0,1	0,1	NUM
fcis-25096	70	17	]	]	PUNCT
fcis-25096	70	18	,	,	PUNCT
fcis-25096	70	19	and	and	CCONJ
fcis-25096	70	20			NOUN
fcis-25096	70	21	is	be	AUX
fcis-25096	70	22	the	the	DET
fcis-25096	70	23	innovation	innovation	NOUN
fcis-25096	70	24	parameter	parameter	NOUN
fcis-25096	70	25	with	with	ADP
fcis-25096	70	26	a	a	DET
fcis-25096	70	27	value	value	NOUN
fcis-25096	70	28	less	less	ADJ
fcis-25096	70	29	than	than	ADP
fcis-25096	70	30	1	1	NUM
fcis-25096	70	31	,	,	PUNCT
fcis-25096	70	32	which	which	PRON
fcis-25096	70	33	performs	perform	VERB
fcis-25096	70	34	contraction	contraction	NOUN
fcis-25096	70	35	and	and	CCONJ
fcis-25096	70	36	expansion	expansion	NOUN
fcis-25096	70	37	operations	operation	NOUN
fcis-25096	70	38	on	on	ADP
fcis-25096	70	39	the	the	DET
fcis-25096	70	40	position	position	NOUN
fcis-25096	70	41	of	of	ADP
fcis-25096	70	42	the	the	DET
fcis-25096	70	43	particles	particle	NOUN
fcis-25096	70	44	.	.	PUNCT
fcis-25096	71	1	3.3	3.3	NUM
fcis-25096	71	2	.	.	PUNCT
fcis-25096	72	1	levy	levy	NOUN
fcis-25096	72	2	flight	flight	NOUN
fcis-25096	72	3	strategy	strategy	NOUN
fcis-25096	72	4	the	the	DET
fcis-25096	72	5	pso	pso	NOUN
fcis-25096	72	6	algorithm	algorithm	NOUN
fcis-25096	72	7	based	base	VERB
fcis-25096	72	8	on	on	ADP
fcis-25096	72	9	quantum	quantum	NOUN
fcis-25096	72	10	computing	compute	VERB
fcis-25096	72	11	optimization	optimization	NOUN
fcis-25096	72	12	only	only	ADV
fcis-25096	72	13	targets	target	VERB
fcis-25096	72	14	non	non	PRON
fcis-25096	72	15	optimal	optimal	ADJ
fcis-25096	72	16	particles	particle	NOUN
fcis-25096	72	17	during	during	ADP
fcis-25096	72	18	each	each	DET
fcis-25096	72	19	evolution	evolution	NOUN
fcis-25096	72	20	,	,	PUNCT
fcis-25096	72	21	and	and	CCONJ
fcis-25096	72	22	can	can	AUX
fcis-25096	72	23	not	not	PART
fcis-25096	72	24	solve	solve	VERB
fcis-25096	72	25	the	the	DET
fcis-25096	72	26	problem	problem	NOUN
fcis-25096	72	27	of	of	ADP
fcis-25096	72	28	optimal	optimal	ADJ
fcis-25096	72	29	particle	particle	NOUN
fcis-25096	72	30	evolution	evolution	NOUN
fcis-25096	72	31	.	.	PUNCT
fcis-25096	73	1	to	to	PART
fcis-25096	73	2	improve	improve	VERB
fcis-25096	73	3	the	the	DET
fcis-25096	73	4	above	above	ADJ
fcis-25096	73	5	shortcomings	shortcoming	NOUN
fcis-25096	73	6	,	,	PUNCT
fcis-25096	73	7	the	the	DET
fcis-25096	73	8	levy	levy	NOUN
fcis-25096	73	9	flight	flight	NOUN
fcis-25096	73	10	strategy[7	strategy[7	NOUN
fcis-25096	73	11	]	]	PUNCT
fcis-25096	73	12	is	be	AUX
fcis-25096	73	13	introduced	introduce	VERB
fcis-25096	73	14	to	to	PART
fcis-25096	73	15	update	update	VERB
fcis-25096	73	16	the	the	DET
fcis-25096	73	17	global	global	ADJ
fcis-25096	73	18	optimal	optimal	ADJ
fcis-25096	73	19	particles	particle	NOUN
fcis-25096	73	20	.	.	PUNCT
fcis-25096	74	1	the	the	DET
fcis-25096	74	2	specific	specific	ADJ
fcis-25096	74	3	idea	idea	NOUN
fcis-25096	74	4	of	of	ADP
fcis-25096	74	5	the	the	DET
fcis-25096	74	6	levy	levy	NOUN
fcis-25096	74	7	flight	flight	NOUN
fcis-25096	74	8	particle	particle	NOUN
fcis-25096	74	9	swarm	swarm	NOUN
fcis-25096	74	10	algorithm	algorithm	NOUN
fcis-25096	74	11	is	be	AUX
fcis-25096	74	12	that	that	SCONJ
fcis-25096	74	13	when	when	SCONJ
fcis-25096	74	14	the	the	DET
fcis-25096	74	15	particle	particle	NOUN
fcis-25096	74	16	swarm	swarm	NOUN
fcis-25096	74	17	algorithm	algorithm	NOUN
fcis-25096	74	18	performs	perform	VERB
fcis-25096	74	19	each	each	DET
fcis-25096	74	20	iteration	iteration	NOUN
fcis-25096	74	21	,	,	PUNCT
fcis-25096	74	22	all	all	DET
fcis-25096	74	23	particles	particle	NOUN
fcis-25096	74	24	will	will	AUX
fcis-25096	74	25	continue	continue	VERB
fcis-25096	74	26	to	to	PART
fcis-25096	74	27	perform	perform	VERB
fcis-25096	74	28	a	a	DET
fcis-25096	74	29	levy	levy	NOUN
fcis-25096	74	30	flight	flight	NOUN
fcis-25096	74	31	,	,	PUNCT
fcis-25096	74	32	utilizing	utilize	VERB
fcis-25096	74	33	the	the	DET
fcis-25096	74	34	characteristics	characteristic	NOUN
fcis-25096	74	35	of	of	ADP
fcis-25096	74	36	levy	levy	NOUN
fcis-25096	74	37	flight	flight	NOUN
fcis-25096	74	38	's	's	PART
fcis-25096	74	39	small	small	ADJ
fcis-25096	74	40	-	-	PUNCT
fcis-25096	74	41	scale	scale	NOUN
fcis-25096	74	42	search	search	NOUN
fcis-25096	74	43	and	and	CCONJ
fcis-25096	74	44	long	long	ADJ
fcis-25096	74	45	-	-	PUNCT
fcis-25096	74	46	distance	distance	NOUN
fcis-25096	74	47	migration	migration	NOUN
fcis-25096	74	48	to	to	PART
fcis-25096	74	49	increase	increase	VERB
fcis-25096	74	50	the	the	DET
fcis-25096	74	51	search	search	NOUN
fcis-25096	74	52	range	range	NOUN
fcis-25096	74	53	,	,	PUNCT
fcis-25096	74	54	avoid	avoid	VERB
fcis-25096	74	55	falling	fall	VERB
fcis-25096	74	56	into	into	ADP
fcis-25096	74	57	local	local	ADJ
fcis-25096	74	58	optima	optima	NOUN
fcis-25096	74	59	,	,	PUNCT
fcis-25096	74	60	and	and	CCONJ
fcis-25096	74	61	enhance	enhance	VERB
fcis-25096	74	62	the	the	DET
fcis-25096	74	63	convergence	convergence	NOUN
fcis-25096	74	64	accuracy	accuracy	NOUN
fcis-25096	74	65	of	of	ADP
fcis-25096	74	66	the	the	DET
fcis-25096	74	67	particle	particle	NOUN
fcis-25096	74	68	swarm	swarm	NOUN
fcis-25096	74	69	algorithm	algorithm	NOUN
fcis-25096	74	70	.	.	PUNCT
fcis-25096	75	1	the	the	DET
fcis-25096	75	2	formula	formula	NOUN
fcis-25096	75	3	for	for	ADP
fcis-25096	75	4	levy	levy	NOUN
fcis-25096	75	5	's	's	PART
fcis-25096	75	6	flight	flight	NOUN
fcis-25096	75	7	strategy	strategy	NOUN
fcis-25096	75	8	step	step	NOUN
fcis-25096	75	9	size	size	NOUN
fcis-25096	75	10	is	be	AUX
fcis-25096	75	11	shown	show	VERB
fcis-25096	75	12	in	in	ADP
fcis-25096	75	13	equation	equation	NOUN
fcis-25096	75	14	(	(	PUNCT
fcis-25096	75	15	7	7	NUM
fcis-25096	75	16	):	):	PUNCT
fcis-25096	75	17	levy	levy	NOUN
fcis-25096	75	18	(	(	PUNCT
fcis-25096	75	19	)	)	PUNCT
fcis-25096	75	20	=	=	SYM
fcis-25096	75	21			PROPN
fcis-25096	75	22			PROPN
fcis-25096	75	23			ADJ
fcis-25096	75	24			PROPN
fcis-25096	75	25	(	(	PUNCT
fcis-25096	75	26	7	7	NUM
fcis-25096	75	27	)	)	PUNCT
fcis-25096	75	28	among	among	ADP
fcis-25096	75	29	them	they	PRON
fcis-25096	75	30	,	,	PUNCT
fcis-25096	75	31			PROPN
fcis-25096	75	32	levy	levy	PROPN
fcis-25096	75	33			PROPN
fcis-25096	75	34	follows	follow	VERB
fcis-25096	75	35	a	a	DET
fcis-25096	75	36	levy	levy	NOUN
fcis-25096	75	37	distribution	distribution	NOUN
fcis-25096	75	38	with	with	ADP
fcis-25096	75	39	parameter	parameter	PROPN
fcis-25096	75	40			PROPN
fcis-25096	75	41	,	,	PUNCT
fcis-25096	75	42	0	0	NUM
fcis-25096	75	43	2	2	NUM
fcis-25096	75	44			PROPN
fcis-25096	75	45	,	,	PUNCT
fcis-25096	75	46	and	and	CCONJ
fcis-25096	75	47			NOUN
fcis-25096	75	48	follows	follow	VERB
fcis-25096	75	49	an	an	DET
fcis-25096	75	50	n	n	CCONJ
fcis-25096	75	51	(	(	PUNCT
fcis-25096	75	52	0,1	0,1	NUM
fcis-25096	75	53	)	)	PUNCT
fcis-25096	75	54	distribution	distribution	NOUN
fcis-25096	75	55	.	.	PUNCT
fcis-25096	76	1	the	the	DET
fcis-25096	76	2	formula	formula	NOUN
fcis-25096	76	3	for	for	ADP
fcis-25096	76	4	updating	update	VERB
fcis-25096	76	5	the	the	DET
fcis-25096	76	6	position	position	NOUN
fcis-25096	76	7	using	use	VERB
fcis-25096	76	8	levy	levy	NOUN
fcis-25096	76	9	flight	flight	NOUN
fcis-25096	76	10	is	be	AUX
fcis-25096	76	11	:	:	PUNCT
fcis-25096	76	12	1	1	NUM
fcis-25096	76	13	(	(	PUNCT
fcis-25096	76	14	)	)	PUNCT
fcis-25096	76	15	(	(	PUNCT
fcis-25096	76	16	(	(	PUNCT
fcis-25096	76	17	)	)	PUNCT
fcis-25096	76	18	)	)	PUNCT
fcis-25096	77	1	t	t	PROPN
fcis-25096	77	2	t	t	PROPN
fcis-25096	77	3	t	t	PROPN
fcis-25096	78	1	i	i	PRON
fcis-25096	79	1	i	i	PRON
fcis-25096	80	1	best	well	ADV
fcis-25096	80	2	ix	ix	ADP
fcis-25096	80	3	x	x	X
fcis-25096	80	4	l	l	NOUN
fcis-25096	80	5	g	g	PROPN
fcis-25096	81	1	i	i	PROPN
fcis-25096	81	2	x	x	PROPN
fcis-25096	81	3			X
fcis-25096	82	1			PROPN
fcis-25096	82	2			ADJ
fcis-25096	82	3			X
fcis-25096	82	4	(	(	PUNCT
fcis-25096	82	5	8)	8)	NUM
fcis-25096	82	6	in	in	ADP
fcis-25096	82	7	the	the	DET
fcis-25096	82	8	formula	formula	NOUN
fcis-25096	82	9	,	,	PUNCT
fcis-25096	82	10	1	1	NUM
fcis-25096	82	11	t	t	NOUN
fcis-25096	82	12	ix	ix	ADP
fcis-25096	82	13			PROPN
fcis-25096	82	14	and	and	CCONJ
fcis-25096	82	15	t	t	PROPN
fcis-25096	82	16	ix	ix	ADV
fcis-25096	82	17	are	be	AUX
fcis-25096	82	18	the	the	DET
fcis-25096	82	19	positions	position	NOUN
fcis-25096	82	20	of	of	ADP
fcis-25096	82	21	the	the	DET
fcis-25096	82	22	t+1st	t+1st	NOUN
fcis-25096	82	23	and	and	CCONJ
fcis-25096	82	24	t	t	NOUN
fcis-25096	82	25	th	th	NOUN
fcis-25096	82	26	generations	generation	NOUN
fcis-25096	82	27	of	of	ADP
fcis-25096	82	28	ix	ix	PROPN
fcis-25096	82	29	,	,	PUNCT
fcis-25096	82	30	respectively	respectively	ADV
fcis-25096	82	31	;	;	PUNCT
fcis-25096	82	32			NUM
fcis-25096	82	33	is	be	AUX
fcis-25096	82	34	the	the	DET
fcis-25096	82	35	scaling	scale	VERB
fcis-25096	82	36	factor	factor	NOUN
fcis-25096	82	37	,	,	PUNCT
fcis-25096	82	38	taken	take	VERB
fcis-25096	82	39	as	as	ADP
fcis-25096	82	40	0.01	0.01	NUM
fcis-25096	82	41	;	;	PUNCT
fcis-25096	82	42	(	(	PUNCT
fcis-25096	82	43	)	)	PUNCT
fcis-25096	82	44	bestg	bestg	PROPN
fcis-25096	82	45	i	i	PRON
fcis-25096	82	46	is	be	AUX
fcis-25096	82	47	the	the	DET
fcis-25096	82	48	optimal	optimal	ADJ
fcis-25096	82	49	position	position	NOUN
fcis-25096	82	50	of	of	ADP
fcis-25096	82	51	the	the	DET
fcis-25096	82	52	current	current	ADJ
fcis-25096	82	53	population	population	NOUN
fcis-25096	82	54	;	;	PUNCT
fcis-25096	82	55	l	l	NOUN
fcis-25096	82	56	is	be	AUX
fcis-25096	82	57	the	the	DET
fcis-25096	82	58	step	step	NOUN
fcis-25096	82	59	size	size	NOUN
fcis-25096	82	60	factor	factor	NOUN
fcis-25096	82	61	that	that	PRON
fcis-25096	82	62	follows	follow	VERB
fcis-25096	82	63	the	the	DET
fcis-25096	82	64	levy	levy	NOUN
fcis-25096	82	65	flight	flight	NOUN
fcis-25096	82	66	strategy	strategy	NOUN
fcis-25096	82	67	,	,	PUNCT
fcis-25096	82	68	as	as	SCONJ
fcis-25096	82	69	shown	show	VERB
fcis-25096	82	70	in	in	ADP
fcis-25096	82	71	the	the	DET
fcis-25096	82	72	following	follow	VERB
fcis-25096	82	73	equation	equation	NOUN
fcis-25096	82	74	:	:	PUNCT
fcis-25096	82	75	1	1	NUM
fcis-25096	82	76	(	(	PUNCT
fcis-25096	82	77	)	)	PUNCT
fcis-25096	82	78	sin	sin	NOUN
fcis-25096	82	79	(	(	PUNCT
fcis-25096	82	80	)	)	PUNCT
fcis-25096	82	81	2~	2~	NOUN
fcis-25096	82	82	.	.	PUNCT
fcis-25096	83	1	l	l	PROPN
fcis-25096	83	2	s	s	PROPN
fcis-25096	83	3			PROPN
fcis-25096	83	4			PROPN
fcis-25096	83	5			PROPN
fcis-25096	83	6			PROPN
fcis-25096	83	7			PUNCT
fcis-25096	83	8			PUNCT
fcis-25096	83	9	(	(	PUNCT
fcis-25096	83	10	9	9	NUM
fcis-25096	83	11	)	)	PUNCT
fcis-25096	83	12	in	in	ADP
fcis-25096	83	13	the	the	DET
fcis-25096	83	14	equation	equation	NOUN
fcis-25096	83	15	,	,	PUNCT
fcis-25096	83	16			PROPN
fcis-25096	83	17	=	=	NOUN
fcis-25096	83	18	1.5	1.5	NUM
fcis-25096	83	19	;	;	PUNCT
fcis-25096	83	20	(	(	PUNCT
fcis-25096	83	21	)	)	PUNCT
fcis-25096	83	22			NOUN
fcis-25096	83	23	is	be	AUX
fcis-25096	83	24	the	the	DET
fcis-25096	83	25	gamma	gamma	PROPN
fcis-25096	83	26	function	function	NOUN
fcis-25096	83	27	.	.	PUNCT
fcis-25096	84	1	due	due	ADP
fcis-25096	84	2	to	to	ADP
fcis-25096	84	3	the	the	DET
fcis-25096	84	4	complexity	complexity	NOUN
fcis-25096	84	5	of	of	ADP
fcis-25096	84	6	the	the	DET
fcis-25096	84	7	levy	levy	NOUN
fcis-25096	84	8	distribution	distribution	NOUN
fcis-25096	84	9	,	,	PUNCT
fcis-25096	84	10	monte	monte	PROPN
fcis-25096	84	11	carlo	carlo	PROPN
fcis-25096	84	12	algorithm	algorithm	PROPN
fcis-25096	84	13	is	be	AUX
fcis-25096	84	14	often	often	ADV
fcis-25096	84	15	used	use	VERB
fcis-25096	84	16	for	for	ADP
fcis-25096	84	17	simulation	simulation	NOUN
fcis-25096	84	18	,	,	PUNCT
fcis-25096	84	19	and	and	CCONJ
fcis-25096	84	20	s	s	VERB
fcis-25096	84	21	is	be	AUX
fcis-25096	84	22	calculated	calculate	VERB
fcis-25096	84	23	using	use	VERB
fcis-25096	84	24	equation	equation	NOUN
fcis-25096	84	25	(	(	PUNCT
fcis-25096	84	26	10	10	NUM
fcis-25096	84	27	):	):	PUNCT
fcis-25096	84	28	1s	1s	NUM
fcis-25096	84	29	v	v	ADP
fcis-25096	84	30			NOUN
fcis-25096	84	31			NOUN
fcis-25096	84	32			NOUN
fcis-25096	84	33	(	(	PUNCT
fcis-25096	84	34	10	10	NUM
fcis-25096	84	35	)	)	PUNCT
fcis-25096	84	36	in	in	ADP
fcis-25096	84	37	the	the	DET
fcis-25096	84	38	formula	formula	NOUN
fcis-25096	84	39	,	,	PUNCT
fcis-25096	84	40			NOUN
fcis-25096	84	41	follows	follow	VERB
fcis-25096	84	42	an	an	DET
fcis-25096	84	43	2(0	2(0	NUM
fcis-25096	84	44	)	)	PUNCT
fcis-25096	84	45	n	n	PRON
fcis-25096	84	46			NOUN
fcis-25096	84	47	，	，	ADJ
fcis-25096	84	48	distribution	distribution	NOUN
fcis-25096	84	49	;	;	PUNCT
fcis-25096	84	50			PROPN
fcis-25096	84	51	follows	follow	VERB
fcis-25096	84	52	(	(	PUNCT
fcis-25096	84	53	0	0	NUM
fcis-25096	84	54	)	)	PUNCT
fcis-25096	84	55	n	n	CCONJ
fcis-25096	84	56	，	，	PROPN
fcis-25096	84	57	1	1	NUM
fcis-25096	84	58	;	;	PUNCT
fcis-25096	84	59			X
fcis-25096	84	60	can	can	AUX
fcis-25096	84	61	be	be	AUX
fcis-25096	84	62	calculated	calculate	VERB
fcis-25096	84	63	by	by	ADP
fcis-25096	84	64	equation	equation	NOUN
fcis-25096	84	65	(	(	PUNCT
fcis-25096	84	66	11	11	NUM
fcis-25096	84	67	):	):	SYM
fcis-25096	84	68	1	1	NUM
fcis-25096	84	69	2	2	NUM
fcis-25096	84	70	(	(	PUNCT
fcis-25096	84	71	1	1	NUM
fcis-25096	84	72	)	)	PUNCT
fcis-25096	84	73	sin	sin	NOUN
fcis-25096	84	74	(	(	PUNCT
fcis-25096	84	75	)	)	PUNCT
fcis-25096	84	76	2	2	NUM
fcis-25096	84	77	1	1	NUM
fcis-25096	84	78	(	(	PUNCT
fcis-25096	84	79	)	)	SYM
fcis-25096	84	80	2	2	NUM
fcis-25096	84	81	2	2	NUM
fcis-25096	84	82			PROPN
fcis-25096	84	83			NOUN
fcis-25096	84	84			NOUN
fcis-25096	84	85			ADJ
fcis-25096	84	86			NOUN
fcis-25096	84	87			NUM
fcis-25096	84	88			ADV
fcis-25096	84	89			PROPN
fcis-25096	84	90			PUNCT
fcis-25096	84	91			PUNCT
fcis-25096	84	92	(	(	PUNCT
fcis-25096	84	93	11	11	NUM
fcis-25096	84	94	)	)	PUNCT
fcis-25096	84	95	3.4	3.4	NUM
fcis-25096	84	96	.	.	PUNCT
fcis-25096	85	1	construction	construction	NOUN
fcis-25096	85	2	of	of	ADP
fcis-25096	85	3	mpso	mpso	ADJ
fcis-25096	85	4	algorithm	algorithm	NOUN
fcis-25096	85	5	path	path	PROPN
fcis-25096	85	6	planning	planning	NOUN
fcis-25096	85	7	model	model	PROPN
fcis-25096	85	8	fig	fig	PROPN
fcis-25096	85	9	2	2	NUM
fcis-25096	85	10	.	.	PUNCT
fcis-25096	86	1	mpso	mpso	ADJ
fcis-25096	86	2	algorithm	algorithm	PROPN
fcis-25096	86	3	path	path	NOUN
fcis-25096	86	4	planning	plan	VERB
fcis-25096	86	5	flowchart	flowchart	PROPN
fcis-25096	86	6	11	11	NUM
fcis-25096	86	7	the	the	DET
fcis-25096	86	8	current	current	ADJ
fcis-25096	86	9	problems	problem	NOUN
fcis-25096	86	10	in	in	ADP
fcis-25096	86	11	path	path	NOUN
fcis-25096	86	12	planning	planning	NOUN
fcis-25096	86	13	mainly	mainly	ADV
fcis-25096	86	14	include	include	VERB
fcis-25096	86	15	difficulty	difficulty	NOUN
fcis-25096	86	16	in	in	ADP
fcis-25096	86	17	environmental	environmental	ADJ
fcis-25096	86	18	modeling	modeling	NOUN
fcis-25096	86	19	,	,	PUNCT
fcis-25096	86	20	slow	slow	ADJ
fcis-25096	86	21	convergence	convergence	NOUN
fcis-25096	86	22	speed	speed	NOUN
fcis-25096	86	23	of	of	ADP
fcis-25096	86	24	algorithms	algorithm	NOUN
fcis-25096	86	25	,	,	PUNCT
fcis-25096	86	26	and	and	CCONJ
fcis-25096	86	27	susceptibility	susceptibility	NOUN
fcis-25096	86	28	to	to	ADP
fcis-25096	86	29	getting	getting	AUX
fcis-25096	86	30	stuck	stick	VERB
fcis-25096	86	31	in	in	ADP
fcis-25096	86	32	local	local	ADJ
fcis-25096	86	33	optimal	optimal	ADJ
fcis-25096	86	34	solutions	solution	NOUN
fcis-25096	86	35	[	[	X
fcis-25096	86	36	8	8	NUM
fcis-25096	86	37	]	]	PUNCT
fcis-25096	86	38	.	.	PUNCT
fcis-25096	87	1	the	the	DET
fcis-25096	87	2	first	first	ADJ
fcis-25096	87	3	step	step	NOUN
fcis-25096	87	4	is	be	AUX
fcis-25096	87	5	to	to	PART
fcis-25096	87	6	initialize	initialize	VERB
fcis-25096	87	7	the	the	DET
fcis-25096	87	8	population	population	NOUN
fcis-25096	87	9	and	and	CCONJ
fcis-25096	87	10	various	various	ADJ
fcis-25096	87	11	parameters	parameter	NOUN
fcis-25096	87	12	using	use	VERB
fcis-25096	87	13	an	an	DET
fcis-25096	87	14	improved	improved	ADJ
fcis-25096	87	15	sine	sine	ADJ
fcis-25096	87	16	chaotic	chaotic	ADJ
fcis-25096	87	17	map	map	NOUN
fcis-25096	87	18	;	;	PUNCT
fcis-25096	87	19	the	the	DET
fcis-25096	87	20	second	second	ADJ
fcis-25096	87	21	step	step	NOUN
fcis-25096	87	22	is	be	AUX
fcis-25096	87	23	to	to	PART
fcis-25096	87	24	calculate	calculate	VERB
fcis-25096	87	25	the	the	DET
fcis-25096	87	26	average	average	NOUN
fcis-25096	87	27	of	of	ADP
fcis-25096	87	28	the	the	DET
fcis-25096	87	29	optimal	optimal	ADJ
fcis-25096	87	30	positions	position	NOUN
fcis-25096	87	31	of	of	ADP
fcis-25096	87	32	particles	particle	NOUN
fcis-25096	87	33	in	in	ADP
fcis-25096	87	34	the	the	DET
fcis-25096	87	35	current	current	ADJ
fcis-25096	87	36	population	population	NOUN
fcis-25096	87	37	;	;	PUNCT
fcis-25096	87	38	step	step	NOUN
fcis-25096	87	39	three	three	NUM
fcis-25096	87	40	,	,	PUNCT
fcis-25096	87	41	calculate	calculate	VERB
fcis-25096	87	42	the	the	DET
fcis-25096	87	43	potential	potential	ADJ
fcis-25096	87	44	field	field	NOUN
fcis-25096	87	45	of	of	ADP
fcis-25096	87	46	the	the	DET
fcis-25096	87	47	i	i	PROPN
fcis-25096	87	48	-	-	PUNCT
fcis-25096	87	49	th	th	X
fcis-25096	87	50	particle	particle	NOUN
fcis-25096	87	51	;	;	PUNCT
fcis-25096	87	52	step	step	NOUN
fcis-25096	87	53	4	4	NUM
fcis-25096	87	54	:	:	PUNCT
fcis-25096	87	55	use	use	VERB
fcis-25096	87	56	monte	monte	PROPN
fcis-25096	87	57	carlo	carlo	PROPN
fcis-25096	87	58	method	method	NOUN
fcis-25096	87	59	to	to	PART
fcis-25096	87	60	update	update	VERB
fcis-25096	87	61	particle	particle	NOUN
fcis-25096	87	62	positions	position	NOUN
fcis-25096	87	63	and	and	CCONJ
fcis-25096	87	64	incorporate	incorporate	VERB
fcis-25096	87	65	levy	levy	NOUN
fcis-25096	87	66	flight	flight	NOUN
fcis-25096	87	67	strategy	strategy	NOUN
fcis-25096	87	68	for	for	ADP
fcis-25096	87	69	optimization	optimization	NOUN
fcis-25096	87	70	;	;	PUNCT
fcis-25096	87	71	step	step	NOUN
fcis-25096	87	72	five	five	NUM
fcis-25096	87	73	,	,	PUNCT
fcis-25096	87	74	calculate	calculate	VERB
fcis-25096	87	75	the	the	DET
fcis-25096	87	76	particle	particle	NOUN
fcis-25096	87	77	position	position	NOUN
fcis-25096	87	78	fitness	fitness	NOUN
fcis-25096	87	79	and	and	CCONJ
fcis-25096	87	80	compare	compare	VERB
fcis-25096	87	81	it	it	PRON
fcis-25096	87	82	with	with	ADP
fcis-25096	87	83	the	the	DET
fcis-25096	87	84	best	good	ADJ
fcis-25096	87	85	position	position	NOUN
fcis-25096	87	86	fitness	fitness	NOUN
fcis-25096	87	87	of	of	ADP
fcis-25096	87	88	individuals	individual	NOUN
fcis-25096	87	89	and	and	CCONJ
fcis-25096	87	90	groups	group	NOUN
fcis-25096	87	91	to	to	PART
fcis-25096	87	92	update	update	VERB
fcis-25096	87	93	the	the	DET
fcis-25096	87	94	optimal	optimal	ADJ
fcis-25096	87	95	position	position	NOUN
fcis-25096	87	96	.	.	PUNCT
fcis-25096	88	1	step	step	NOUN
fcis-25096	88	2	six	six	NUM
fcis-25096	88	3	,	,	PUNCT
fcis-25096	88	4	determine	determine	VERB
fcis-25096	88	5	whether	whether	SCONJ
fcis-25096	88	6	the	the	DET
fcis-25096	88	7	iteration	iteration	NOUN
fcis-25096	88	8	termination	termination	NOUN
fcis-25096	88	9	condition	condition	NOUN
fcis-25096	88	10	is	be	AUX
fcis-25096	88	11	met	meet	VERB
fcis-25096	88	12	.	.	PUNCT
fcis-25096	89	1	if	if	SCONJ
fcis-25096	89	2	not	not	PART
fcis-25096	89	3	,	,	PUNCT
fcis-25096	89	4	return	return	VERB
fcis-25096	89	5	to	to	PART
fcis-25096	89	6	step	step	VERB
fcis-25096	89	7	two	two	NUM
fcis-25096	89	8	.	.	PUNCT
fcis-25096	90	1	the	the	DET
fcis-25096	90	2	mpso	mpso	ADJ
fcis-25096	90	3	algorithm	algorithm	PROPN
fcis-25096	90	4	path	path	PROPN
fcis-25096	90	5	planning	planning	NOUN
fcis-25096	90	6	flowchart	flowchart	PROPN
fcis-25096	90	7	is	be	AUX
fcis-25096	90	8	shown	show	VERB
fcis-25096	90	9	in	in	ADP
fcis-25096	90	10	figure	figure	NOUN
fcis-25096	90	11	2	2	NUM
fcis-25096	90	12	:	:	SYM
fcis-25096	90	13	4	4	NUM
fcis-25096	90	14	.	.	X
fcis-25096	90	15	simulation	simulation	NOUN
fcis-25096	90	16	experiments	experiment	NOUN
fcis-25096	90	17	and	and	CCONJ
fcis-25096	90	18	result	result	VERB
fcis-25096	90	19	analysis	analysis	NOUN
fcis-25096	90	20	the	the	DET
fcis-25096	90	21	experimental	experimental	ADJ
fcis-25096	90	22	platform	platform	NOUN
fcis-25096	90	23	selected	select	VERB
fcis-25096	90	24	is	be	AUX
fcis-25096	90	25	matlab	matlab	PROPN
fcis-25096	90	26	2022b	2022b	NUM
fcis-25096	90	27	,	,	PUNCT
fcis-25096	90	28	with	with	ADP
fcis-25096	90	29	a	a	DET
fcis-25096	90	30	computer	computer	NOUN
fcis-25096	90	31	cpu	cpu	NOUN
fcis-25096	90	32	model	model	NOUN
fcis-25096	90	33	of	of	ADP
fcis-25096	90	34	i7	i7	NOUN
fcis-25096	90	35	-	-	PUNCT
fcis-25096	90	36	13700k	13700k	NUM
fcis-25096	90	37	and	and	CCONJ
fcis-25096	90	38	a	a	DET
fcis-25096	90	39	running	running	NOUN
fcis-25096	90	40	memory	memory	NOUN
fcis-25096	90	41	of	of	ADP
fcis-25096	90	42	32	32	NUM
fcis-25096	90	43	gb	gb	NOUN
fcis-25096	90	44	.	.	PUNCT
fcis-25096	91	1	in	in	ADP
fcis-25096	91	2	order	order	NOUN
fcis-25096	91	3	to	to	PART
fcis-25096	91	4	verify	verify	VERB
fcis-25096	91	5	the	the	DET
fcis-25096	91	6	effectiveness	effectiveness	NOUN
fcis-25096	91	7	of	of	ADP
fcis-25096	91	8	the	the	DET
fcis-25096	91	9	algorithm	algorithm	NOUN
fcis-25096	91	10	proposed	propose	VERB
fcis-25096	91	11	in	in	ADP
fcis-25096	91	12	this	this	DET
fcis-25096	91	13	paper	paper	NOUN
fcis-25096	91	14	,	,	PUNCT
fcis-25096	91	15	path	path	NOUN
fcis-25096	91	16	planning	planning	NOUN
fcis-25096	91	17	for	for	ADP
fcis-25096	91	18	mobile	mobile	ADJ
fcis-25096	91	19	robots	robot	NOUN
fcis-25096	91	20	was	be	AUX
fcis-25096	91	21	conducted	conduct	VERB
fcis-25096	91	22	in	in	ADP
fcis-25096	91	23	two	two	NUM
fcis-25096	91	24	different	different	ADJ
fcis-25096	91	25	grid	grid	NOUN
fcis-25096	91	26	environments	environment	NOUN
fcis-25096	91	27	,	,	PUNCT
fcis-25096	91	28	and	and	CCONJ
fcis-25096	91	29	simulation	simulation	NOUN
fcis-25096	91	30	comparative	comparative	ADJ
fcis-25096	91	31	experiments	experiment	NOUN
fcis-25096	91	32	were	be	AUX
fcis-25096	91	33	conducted	conduct	VERB
fcis-25096	91	34	using	use	VERB
fcis-25096	91	35	the	the	DET
fcis-25096	91	36	algorithm	algorithm	NOUN
fcis-25096	91	37	proposed	propose	VERB
fcis-25096	91	38	in	in	ADP
fcis-25096	91	39	this	this	DET
fcis-25096	91	40	paper	paper	NOUN
fcis-25096	91	41	with	with	ADP
fcis-25096	91	42	pso	pso	NOUN
fcis-25096	91	43	,	,	PUNCT
fcis-25096	91	44	aco_ga	aco_ga	PROPN
fcis-25096	91	45	,	,	PUNCT
fcis-25096	91	46	soa	soa	NOUN
fcis-25096	91	47	,	,	PUNCT
fcis-25096	91	48	and	and	CCONJ
fcis-25096	91	49	ga	ga	PROPN
fcis-25096	91	50	algorithms	algorithm	NOUN
fcis-25096	91	51	.	.	PUNCT
fcis-25096	92	1	4.1	4.1	NUM
fcis-25096	92	2	.	.	PUNCT
fcis-25096	92	3	environmental	environmental	ADJ
fcis-25096	92	4	map	map	NOUN
fcis-25096	92	5	this	this	DET
fcis-25096	92	6	article	article	NOUN
fcis-25096	92	7	maps	map	VERB
fcis-25096	92	8	the	the	DET
fcis-25096	92	9	environment	environment	NOUN
fcis-25096	92	10	to	to	ADP
fcis-25096	92	11	a	a	DET
fcis-25096	92	12	grid	grid	NOUN
fcis-25096	92	13	map[9	map[9	NOUN
fcis-25096	92	14	]	]	X
fcis-25096	92	15	,	,	PUNCT
fcis-25096	92	16	which	which	PRON
fcis-25096	92	17	equates	equate	VERB
fcis-25096	92	18	the	the	DET
fcis-25096	92	19	environment	environment	NOUN
fcis-25096	92	20	to	to	ADP
fcis-25096	92	21	a	a	DET
fcis-25096	92	22	grid	grid	NOUN
fcis-25096	92	23	matrix	matrix	NOUN
fcis-25096	92	24	composed	compose	VERB
fcis-25096	92	25	of	of	ADP
fcis-25096	92	26	cells	cell	NOUN
fcis-25096	92	27	of	of	ADP
fcis-25096	92	28	the	the	DET
fcis-25096	92	29	same	same	ADJ
fcis-25096	92	30	size	size	NOUN
fcis-25096	92	31	.	.	PUNCT
fcis-25096	93	1	build	build	VERB
fcis-25096	93	2	two	two	NUM
fcis-25096	93	3	maps	map	NOUN
fcis-25096	93	4	of	of	ADP
fcis-25096	93	5	different	different	ADJ
fcis-25096	93	6	complexities	complexity	NOUN
fcis-25096	93	7	using	use	VERB
fcis-25096	93	8	matlab	matlab	PROPN
fcis-25096	93	9	,	,	PUNCT
fcis-25096	93	10	encode	encode	VERB
fcis-25096	93	11	each	each	DET
fcis-25096	93	12	grid	grid	NOUN
fcis-25096	93	13	with	with	ADP
fcis-25096	93	14	real	real	ADJ
fcis-25096	93	15	numbers	number	NOUN
fcis-25096	93	16	,	,	PUNCT
fcis-25096	93	17	fill	fill	VERB
fcis-25096	93	18	the	the	DET
fcis-25096	93	19	grids	grid	NOUN
fcis-25096	93	20	corresponding	correspond	VERB
fcis-25096	93	21	to	to	ADP
fcis-25096	93	22	actual	actual	ADJ
fcis-25096	93	23	obstacles	obstacle	NOUN
fcis-25096	93	24	with	with	ADP
fcis-25096	93	25	1	1	NUM
fcis-25096	93	26	,	,	PUNCT
fcis-25096	93	27	represent	represent	VERB
fcis-25096	93	28	them	they	PRON
fcis-25096	93	29	in	in	ADP
fcis-25096	93	30	red	red	NOUN
fcis-25096	93	31	on	on	ADP
fcis-25096	93	32	the	the	DET
fcis-25096	93	33	map	map	NOUN
fcis-25096	93	34	,	,	PUNCT
fcis-25096	93	35	fill	fill	VERB
fcis-25096	93	36	feasible	feasible	ADJ
fcis-25096	93	37	areas	area	NOUN
fcis-25096	93	38	with	with	ADP
fcis-25096	93	39	0	0	NUM
fcis-25096	93	40	,	,	PUNCT
fcis-25096	93	41	and	and	CCONJ
fcis-25096	93	42	represent	represent	VERB
fcis-25096	93	43	them	they	PRON
fcis-25096	93	44	in	in	ADP
fcis-25096	93	45	white	white	ADJ
fcis-25096	93	46	.	.	PUNCT
fcis-25096	94	1	the	the	DET
fcis-25096	94	2	side	side	NOUN
fcis-25096	94	3	length	length	NOUN
fcis-25096	94	4	of	of	ADP
fcis-25096	94	5	each	each	DET
fcis-25096	94	6	grid	grid	NOUN
fcis-25096	94	7	is	be	AUX
fcis-25096	94	8	1	1	NUM
fcis-25096	94	9	,	,	PUNCT
fcis-25096	94	10	where	where	SCONJ
fcis-25096	94	11	the	the	DET
fcis-25096	94	12	point	point	NOUN
fcis-25096	94	13	with	with	ADP
fcis-25096	94	14	a	a	DET
fcis-25096	94	15	real	real	ADJ
fcis-25096	94	16	number	number	NOUN
fcis-25096	94	17	code	code	NOUN
fcis-25096	94	18	of	of	ADP
fcis-25096	94	19	0	0	NUM
fcis-25096	94	20	represents	represent	VERB
fcis-25096	94	21	the	the	DET
fcis-25096	94	22	starting	starting	NOUN
fcis-25096	94	23	point	point	NOUN
fcis-25096	94	24	of	of	ADP
fcis-25096	94	25	the	the	DET
fcis-25096	94	26	robot	robot	NOUN
fcis-25096	94	27	,	,	PUNCT
fcis-25096	94	28	and	and	CCONJ
fcis-25096	94	29	the	the	DET
fcis-25096	94	30	point	point	NOUN
fcis-25096	94	31	with	with	ADP
fcis-25096	94	32	a	a	DET
fcis-25096	94	33	real	real	ADJ
fcis-25096	94	34	number	number	NOUN
fcis-25096	94	35	code	code	NOUN
fcis-25096	94	36	of	of	ADP
fcis-25096	94	37	399	399	NUM
fcis-25096	94	38	represents	represent	VERB
fcis-25096	94	39	the	the	DET
fcis-25096	94	40	target	target	NOUN
fcis-25096	94	41	point[10	point[10	ADV
fcis-25096	94	42	]	]	X
fcis-25096	94	43	.	.	PUNCT
fcis-25096	95	1	the	the	DET
fcis-25096	95	2	grid	grid	NOUN
fcis-25096	95	3	map	map	NOUN
fcis-25096	95	4	is	be	AUX
fcis-25096	95	5	shown	show	VERB
fcis-25096	95	6	in	in	ADP
fcis-25096	95	7	figure	figure	NOUN
fcis-25096	95	8	3	3	NUM
fcis-25096	95	9	.	.	PUNCT
fcis-25096	95	10	fig	fig	NOUN
fcis-25096	95	11	3	3	X
fcis-25096	95	12	.	.	PUNCT
fcis-25096	95	13	grid	grid	NOUN
fcis-25096	95	14	maps	map	NOUN
fcis-25096	95	15	4.2	4.2	NUM
fcis-25096	95	16	.	.	PUNCT
fcis-25096	96	1	analysis	analysis	NOUN
fcis-25096	96	2	of	of	ADP
fcis-25096	96	3	path	path	NOUN
fcis-25096	96	4	planning	planning	NOUN
fcis-25096	96	5	experiment	experiment	NOUN
fcis-25096	96	6	results	result	NOUN
fcis-25096	96	7	fig	fig	NOUN
fcis-25096	96	8	4	4	NUM
fcis-25096	96	9	.	.	NOUN
fcis-25096	96	10	comparison	comparison	NOUN
fcis-25096	96	11	and	and	CCONJ
fcis-25096	96	12	optimization	optimization	NOUN
fcis-25096	96	13	of	of	ADP
fcis-25096	96	14	iterative	iterative	NOUN
fcis-25096	96	15	curves	curve	NOUN
fcis-25096	96	16	using	use	VERB
fcis-25096	96	17	multiple	multiple	ADJ
fcis-25096	96	18	intelligent	intelligent	ADJ
fcis-25096	96	19	algorithms	algorithm	NOUN
fcis-25096	96	20	in	in	ADP
fcis-25096	96	21	order	order	NOUN
fcis-25096	96	22	to	to	PART
fcis-25096	96	23	verify	verify	VERB
fcis-25096	96	24	the	the	DET
fcis-25096	96	25	performance	performance	NOUN
fcis-25096	96	26	of	of	ADP
fcis-25096	96	27	the	the	DET
fcis-25096	96	28	proposed	propose	VERB
fcis-25096	96	29	mpso	mpso	ADJ
fcis-25096	96	30	algorithm	algorithm	NOUN
fcis-25096	96	31	,	,	PUNCT
fcis-25096	96	32	during	during	ADP
fcis-25096	96	33	the	the	DET
fcis-25096	96	34	simulation	simulation	NOUN
fcis-25096	96	35	comparison	comparison	NOUN
fcis-25096	96	36	experiment	experiment	NOUN
fcis-25096	96	37	,	,	PUNCT
fcis-25096	96	38	the	the	DET
fcis-25096	96	39	algorithm	algorithm	NOUN
fcis-25096	96	40	proposed	propose	VERB
fcis-25096	96	41	in	in	ADP
fcis-25096	96	42	this	this	DET
fcis-25096	96	43	paper	paper	NOUN
fcis-25096	96	44	was	be	AUX
fcis-25096	96	45	used	use	VERB
fcis-25096	96	46	in	in	ADP
fcis-25096	96	47	the	the	DET
fcis-25096	96	48	iterative	iterative	NOUN
fcis-25096	96	49	process	process	NOUN
fcis-25096	96	50	of	of	ADP
fcis-25096	96	51	robot	robot	NOUN
fcis-25096	96	52	path	path	NOUN
fcis-25096	96	53	planning	planning	PROPN
fcis-25096	96	54	with	with	ADP
fcis-25096	96	55	pso	pso	NOUN
fcis-25096	96	56	,	,	PUNCT
fcis-25096	96	57	aco_ga	aco_ga	PROPN
fcis-25096	96	58	,	,	PUNCT
fcis-25096	96	59	soa	soa	NOUN
fcis-25096	96	60	,	,	PUNCT
fcis-25096	96	61	and	and	CCONJ
fcis-25096	96	62	ga[11	ga[11	PRON
fcis-25096	96	63	]	]	X
fcis-25096	96	64	.	.	PUNCT
fcis-25096	97	1	the	the	DET
fcis-25096	97	2	fitness	fitness	NOUN
fcis-25096	97	3	iteration	iteration	NOUN
fcis-25096	97	4	process	process	NOUN
fcis-25096	97	5	of	of	ADP
fcis-25096	97	6	each	each	DET
fcis-25096	97	7	algorithm	algorithm	NOUN
fcis-25096	97	8	was	be	AUX
fcis-25096	97	9	compared	compare	VERB
fcis-25096	97	10	as	as	SCONJ
fcis-25096	97	11	shown	show	VERB
fcis-25096	97	12	in	in	ADP
fcis-25096	97	13	figure	figure	NOUN
fcis-25096	97	14	4	4	NUM
fcis-25096	97	15	.	.	PUNCT
fcis-25096	97	16	from	from	ADP
fcis-25096	97	17	the	the	DET
fcis-25096	97	18	figure	figure	NOUN
fcis-25096	97	19	,	,	PUNCT
fcis-25096	97	20	it	it	PRON
fcis-25096	97	21	can	can	AUX
fcis-25096	97	22	be	be	AUX
fcis-25096	97	23	seen	see	VERB
fcis-25096	97	24	that	that	SCONJ
fcis-25096	97	25	the	the	DET
fcis-25096	97	26	mpso	mpso	ADJ
fcis-25096	97	27	algorithm	algorithm	NOUN
fcis-25096	97	28	has	have	VERB
fcis-25096	97	29	a	a	DET
fcis-25096	97	30	faster	fast	ADJ
fcis-25096	97	31	number	number	NOUN
fcis-25096	97	32	of	of	ADP
fcis-25096	97	33	iterations	iteration	NOUN
fcis-25096	97	34	.	.	PUNCT
fcis-25096	98	1	compared	compare	VERB
fcis-25096	98	2	to	to	ADP
fcis-25096	98	3	a	a	DET
fcis-25096	98	4	single	single	ADJ
fcis-25096	98	5	intelligent	intelligent	ADJ
fcis-25096	98	6	algorithm	algorithm	NOUN
fcis-25096	98	7	,	,	PUNCT
fcis-25096	98	8	it	it	PRON
fcis-25096	98	9	is	be	AUX
fcis-25096	98	10	slightly	slightly	ADV
fcis-25096	98	11	slower	slow	ADJ
fcis-25096	98	12	due	due	ADP
fcis-25096	98	13	to	to	ADP
fcis-25096	98	14	the	the	DET
fcis-25096	98	15	integration	integration	NOUN
fcis-25096	98	16	of	of	ADP
fcis-25096	98	17	multiple	multiple	ADJ
fcis-25096	98	18	improvement	improvement	NOUN
fcis-25096	98	19	strategies	strategy	NOUN
fcis-25096	98	20	,	,	PUNCT
fcis-25096	98	21	which	which	PRON
fcis-25096	98	22	increases	increase	VERB
fcis-25096	98	23	complexity	complexity	NOUN
fcis-25096	98	24	.	.	PUNCT
fcis-25096	99	1	however	however	ADV
fcis-25096	99	2	,	,	PUNCT
fcis-25096	99	3	for	for	ADP
fcis-25096	99	4	the	the	DET
fcis-25096	99	5	fusion	fusion	NOUN
fcis-25096	99	6	of	of	ADP
fcis-25096	99	7	multiple	multiple	ADJ
fcis-25096	99	8	intelligent	intelligent	ADJ
fcis-25096	99	9	algorithms	algorithm	NOUN
fcis-25096	99	10	,	,	PUNCT
fcis-25096	99	11	it	it	PRON
fcis-25096	99	12	is	be	AUX
fcis-25096	99	13	significantly	significantly	ADV
fcis-25096	99	14	faster	fast	ADJ
fcis-25096	99	15	.	.	PUNCT
fcis-25096	100	1	path	path	NOUN
fcis-25096	100	2	planning	planning	NOUN
fcis-25096	100	3	simulation	simulation	NOUN
fcis-25096	100	4	experiments	experiment	NOUN
fcis-25096	100	5	were	be	AUX
fcis-25096	100	6	conducted	conduct	VERB
fcis-25096	100	7	20	20	NUM
fcis-25096	100	8	times	time	NOUN
fcis-25096	100	9	on	on	ADP
fcis-25096	100	10	the	the	DET
fcis-25096	100	11	grid	grid	NOUN
fcis-25096	100	12	maps	map	NOUN
fcis-25096	100	13	shown	show	VERB
fcis-25096	100	14	in	in	ADP
fcis-25096	100	15	figure	figure	NOUN
fcis-25096	100	16	3	3	NUM
fcis-25096	100	17	(	(	PUNCT
fcis-25096	100	18	a	a	NOUN
fcis-25096	100	19	)	)	PUNCT
fcis-25096	100	20	and	and	CCONJ
fcis-25096	100	21	(	(	PUNCT
fcis-25096	100	22	b	b	NOUN
fcis-25096	100	23	)	)	PUNCT
fcis-25096	100	24	,	,	PUNCT
fcis-25096	100	25	with	with	ADP
fcis-25096	100	26	an	an	DET
fcis-25096	100	27	initial	initial	ADJ
fcis-25096	100	28	particle	particle	NOUN
fcis-25096	100	29	count	count	NOUN
fcis-25096	100	30	of	of	ADP
fcis-25096	100	31	50	50	NUM
fcis-25096	100	32	and	and	CCONJ
fcis-25096	100	33	200	200	NUM
fcis-25096	100	34	iterations	iteration	NOUN
fcis-25096	100	35	.	.	PUNCT
fcis-25096	101	1	each	each	DET
fcis-25096	101	2	algorithm	algorithm	NOUN
fcis-25096	101	3	was	be	AUX
fcis-25096	101	4	compared	compare	VERB
fcis-25096	101	5	under	under	ADP
fcis-25096	101	6	the	the	DET
fcis-25096	101	7	same	same	ADJ
fcis-25096	101	8	parameters	parameter	NOUN
fcis-25096	101	9	,	,	PUNCT
fcis-25096	101	10	and	and	CCONJ
fcis-25096	101	11	the	the	DET
fcis-25096	101	12	shortest	short	ADJ
fcis-25096	101	13	path	path	NOUN
fcis-25096	101	14	,	,	PUNCT
fcis-25096	101	15	average	average	ADJ
fcis-25096	101	16	time	time	NOUN
fcis-25096	101	17	,	,	PUNCT
fcis-25096	101	18	and	and	CCONJ
fcis-25096	101	19	average	average	ADJ
fcis-25096	101	20	path	path	NOUN
fcis-25096	101	21	were	be	AUX
fcis-25096	101	22	recorded	record	VERB
fcis-25096	101	23	.	.	PUNCT
fcis-25096	102	1	the	the	DET
fcis-25096	102	2	path	path	NOUN
fcis-25096	102	3	planning	planning	NOUN
fcis-25096	102	4	results	result	NOUN
fcis-25096	102	5	in	in	ADP
fcis-25096	102	6	figure	figure	NOUN
fcis-25096	102	7	3	3	NUM
fcis-25096	102	8	(	(	PUNCT
fcis-25096	102	9	a	a	NOUN
fcis-25096	102	10	)	)	PUNCT
fcis-25096	102	11	are	be	AUX
fcis-25096	102	12	shown	show	VERB
fcis-25096	102	13	in	in	ADP
fcis-25096	102	14	table	table	NOUN
fcis-25096	102	15	1	1	NUM
fcis-25096	102	16	,	,	PUNCT
fcis-25096	102	17	and	and	CCONJ
fcis-25096	102	18	the	the	DET
fcis-25096	102	19	planned	plan	VERB
fcis-25096	102	20	path	path	NOUN
fcis-25096	102	21	is	be	AUX
fcis-25096	102	22	shown	show	VERB
fcis-25096	102	23	in	in	ADP
fcis-25096	102	24	figure	figure	NOUN
fcis-25096	102	25	5	5	NUM
fcis-25096	102	26	.	.	PUNCT
fcis-25096	102	27	table	table	NOUN
fcis-25096	102	28	1	1	NUM
fcis-25096	102	29	.	.	PUNCT
fcis-25096	103	1	comparison	comparison	NOUN
fcis-25096	103	2	of	of	ADP
fcis-25096	103	3	path	path	NOUN
fcis-25096	103	4	planning	planning	NOUN
fcis-25096	103	5	results	result	NOUN
fcis-25096	103	6	algorithm	algorithm	PROPN
fcis-25096	103	7	shortest	short	ADJ
fcis-25096	103	8	path	path	NOUN
fcis-25096	103	9	/cm	/cm	PUNCT
fcis-25096	104	1	average	average	ADJ
fcis-25096	104	2	path	path	NOUN
fcis-25096	104	3	/cm	/cm	PUNCT
fcis-25096	105	1	average	average	ADJ
fcis-25096	105	2	time	time	NOUN
fcis-25096	105	3	/	/	SYM
fcis-25096	105	4	s	s	PART
fcis-25096	105	5	mpso	mpso	ADJ
fcis-25096	105	6	28.6.025	28.6.025	NUM
fcis-25096	105	7	28.5827	28.5827	NUM
fcis-25096	105	8	1.3476	1.3476	NUM
fcis-25096	105	9	pso	pso	NOUN
fcis-25096	105	10	30.05	30.05	NUM
fcis-25096	105	11	31.668	31.668	NUM
fcis-25096	105	12	0.18252	0.18252	NUM
fcis-25096	105	13	ga	ga	NOUN
fcis-25096	105	14	32.054	32.054	NUM
fcis-25096	105	15	34.9618	34.9618	NUM
fcis-25096	105	16	2.7102	2.7102	NUM
fcis-25096	105	17	aco_ga	aco_ga	PROPN
fcis-25096	105	18	27.4663	27.4663	NUM
fcis-25096	105	19	28.4327	28.4327	NUM
fcis-25096	105	20	2.1453	2.1453	NUM
fcis-25096	105	21	soa	soa	NOUN
fcis-25096	105	22	29.3832	29.3832	NUM
fcis-25096	105	23	31.7202	31.7202	NUM
fcis-25096	105	24	0.66871	0.66871	NUM
fcis-25096	105	25	0	0	NUM
fcis-25096	105	26	20	20	NUM
fcis-25096	105	27	40	40	NUM
fcis-25096	105	28	60	60	NUM
fcis-25096	105	29	80	80	NUM
fcis-25096	105	30	100	100	NUM
fcis-25096	105	31	120	120	NUM
fcis-25096	105	32	140	140	NUM
fcis-25096	105	33	160	160	NUM
fcis-25096	105	34	180	180	NUM
fcis-25096	105	35	200	200	NUM
fcis-25096	105	36	iterations	iteration	NOUN
fcis-25096	105	37	0	0	NUM
fcis-25096	105	38	100	100	NUM
fcis-25096	105	39	200	200	NUM
fcis-25096	105	40	300	300	NUM
fcis-25096	105	41	400	400	NUM
fcis-25096	105	42	500	500	NUM
fcis-25096	105	43	600	600	NUM
fcis-25096	105	44	700	700	NUM
fcis-25096	105	45	800	800	NUM
fcis-25096	105	46	f	f	NOUN
fcis-25096	105	47	itn	itn	NOUN
fcis-25096	105	48	es	es	X
fcis-25096	105	49	s	s	PROPN
fcis-25096	105	50	va	va	PROPN
fcis-25096	105	51	lu	lu	PROPN
fcis-25096	105	52	e	e	PROPN
fcis-25096	105	53	pso	pso	PROPN
fcis-25096	105	54	aco	aco	PROPN
fcis-25096	105	55	-	-	PUNCT
fcis-25096	105	56	ga	ga	PROPN
fcis-25096	105	57	mpso	mpso	PROPN
fcis-25096	105	58	ga	ga	PROPN
fcis-25096	105	59	soa	soa	PROPN
fcis-25096	105	60	12	12	NUM
fcis-25096	105	61	fig	fig	NOUN
fcis-25096	105	62	5	5	NUM
fcis-25096	105	63	.	.	PUNCT
fcis-25096	105	64	simple	simple	ADJ
fcis-25096	105	65	map	map	NOUN
fcis-25096	105	66	model	model	NOUN
fcis-25096	105	67	with	with	ADP
fcis-25096	105	68	multiple	multiple	ADJ
fcis-25096	105	69	algorithm	algorithm	NOUN
fcis-25096	105	70	path	path	NOUN
fcis-25096	105	71	planning	planning	NOUN
fcis-25096	105	72	results	result	NOUN
fcis-25096	105	73	from	from	ADP
fcis-25096	105	74	table	table	NOUN
fcis-25096	105	75	1	1	NUM
fcis-25096	105	76	,	,	PUNCT
fcis-25096	105	77	it	it	PRON
fcis-25096	105	78	can	can	AUX
fcis-25096	105	79	be	be	AUX
fcis-25096	105	80	observed	observe	VERB
fcis-25096	105	81	that	that	SCONJ
fcis-25096	105	82	each	each	DET
fcis-25096	105	83	algorithm	algorithm	NOUN
fcis-25096	105	84	underwent	undergo	VERB
fcis-25096	105	85	20	20	NUM
fcis-25096	105	86	rounds	round	NOUN
fcis-25096	105	87	of	of	ADP
fcis-25096	105	88	planning	planning	NOUN
fcis-25096	105	89	,	,	PUNCT
fcis-25096	105	90	and	and	CCONJ
fcis-25096	105	91	the	the	DET
fcis-25096	105	92	average	average	ADJ
fcis-25096	105	93	time	time	NOUN
fcis-25096	105	94	recorded	record	VERB
fcis-25096	105	95	the	the	DET
fcis-25096	105	96	efficiency	efficiency	NOUN
fcis-25096	105	97	of	of	ADP
fcis-25096	105	98	each	each	DET
fcis-25096	105	99	algorithm	algorithm	NOUN
fcis-25096	105	100	.	.	PUNCT
fcis-25096	106	1	among	among	ADP
fcis-25096	106	2	them	they	PRON
fcis-25096	106	3	,	,	PUNCT
fcis-25096	106	4	mpso	mpso	ADJ
fcis-25096	106	5	algorithm	algorithm	NOUN
fcis-25096	106	6	has	have	VERB
fcis-25096	106	7	a	a	DET
fcis-25096	106	8	slower	slow	ADJ
fcis-25096	106	9	convergence	convergence	NOUN
fcis-25096	106	10	speed	speed	NOUN
fcis-25096	106	11	and	and	CCONJ
fcis-25096	106	12	longer	long	ADJ
fcis-25096	106	13	time	time	NOUN
fcis-25096	106	14	compared	compare	VERB
fcis-25096	106	15	to	to	ADP
fcis-25096	106	16	pso	pso	NOUN
fcis-25096	106	17	algorithm	algorithm	NOUN
fcis-25096	106	18	and	and	CCONJ
fcis-25096	106	19	soa	soa	PROPN
fcis-25096	106	20	algorithm	algorithm	NOUN
fcis-25096	106	21	,	,	PUNCT
fcis-25096	106	22	but	but	CCONJ
fcis-25096	106	23	the	the	DET
fcis-25096	106	24	planned	plan	VERB
fcis-25096	106	25	shortest	short	ADJ
fcis-25096	106	26	path	path	NOUN
fcis-25096	106	27	and	and	CCONJ
fcis-25096	106	28	average	average	ADJ
fcis-25096	106	29	path	path	NOUN
fcis-25096	106	30	are	be	AUX
fcis-25096	106	31	much	much	ADV
fcis-25096	106	32	higher	high	ADJ
fcis-25096	106	33	than	than	ADP
fcis-25096	106	34	these	these	DET
fcis-25096	106	35	two	two	NUM
fcis-25096	106	36	algorithms	algorithm	NOUN
fcis-25096	106	37	,	,	PUNCT
fcis-25096	106	38	demonstrating	demonstrate	VERB
fcis-25096	106	39	better	well	ADJ
fcis-25096	106	40	search	search	NOUN
fcis-25096	106	41	accuracy	accuracy	NOUN
fcis-25096	106	42	and	and	CCONJ
fcis-25096	106	43	optimization	optimization	NOUN
fcis-25096	106	44	ability	ability	NOUN
fcis-25096	106	45	.	.	PUNCT
fcis-25096	107	1	compared	compare	VERB
fcis-25096	107	2	to	to	ADP
fcis-25096	107	3	mpso	mpso	ADJ
fcis-25096	107	4	algorithm	algorithm	NOUN
fcis-25096	107	5	,	,	PUNCT
fcis-25096	107	6	aoc_ga	aoc_ga	PROPN
fcis-25096	107	7	algorithm	algorithm	PROPN
fcis-25096	107	8	has	have	VERB
fcis-25096	107	9	better	well	ADJ
fcis-25096	107	10	solution	solution	NOUN
fcis-25096	107	11	quality	quality	NOUN
fcis-25096	107	12	,	,	PUNCT
fcis-25096	107	13	but	but	CCONJ
fcis-25096	107	14	its	its	PRON
fcis-25096	107	15	optimization	optimization	NOUN
fcis-25096	107	16	efficiency	efficiency	NOUN
fcis-25096	107	17	is	be	AUX
fcis-25096	107	18	lower	low	ADJ
fcis-25096	107	19	than	than	ADP
fcis-25096	107	20	mpso	mpso	ADJ
fcis-25096	107	21	algorithm[12	algorithm[12	PROPN
fcis-25096	107	22	]	]	PUNCT
fcis-25096	107	23	.	.	PUNCT
fcis-25096	108	1	therefore	therefore	ADV
fcis-25096	108	2	,	,	PUNCT
fcis-25096	108	3	the	the	DET
fcis-25096	108	4	mpso	mpso	ADJ
fcis-25096	108	5	algorithm	algorithm	NOUN
fcis-25096	108	6	proposed	propose	VERB
fcis-25096	108	7	in	in	ADP
fcis-25096	108	8	this	this	DET
fcis-25096	108	9	article	article	NOUN
fcis-25096	108	10	not	not	PART
fcis-25096	108	11	only	only	ADV
fcis-25096	108	12	plans	plan	VERB
fcis-25096	108	13	shorter	short	ADJ
fcis-25096	108	14	paths	path	NOUN
fcis-25096	108	15	,	,	PUNCT
fcis-25096	108	16	but	but	CCONJ
fcis-25096	108	17	also	also	ADV
fcis-25096	108	18	has	have	VERB
fcis-25096	108	19	high	high	ADJ
fcis-25096	108	20	efficiency	efficiency	NOUN
fcis-25096	108	21	and	and	CCONJ
fcis-25096	108	22	good	good	ADJ
fcis-25096	108	23	robustness	robustness	NOUN
fcis-25096	108	24	.	.	PUNCT
fcis-25096	109	1	figure	figure	NOUN
fcis-25096	109	2	5	5	NUM
fcis-25096	109	3	shows	show	VERB
fcis-25096	109	4	the	the	DET
fcis-25096	109	5	optimal	optimal	ADJ
fcis-25096	109	6	routes	route	NOUN
fcis-25096	109	7	planned	plan	VERB
fcis-25096	109	8	by	by	ADP
fcis-25096	109	9	various	various	ADJ
fcis-25096	109	10	algorithms	algorithm	NOUN
fcis-25096	109	11	in	in	ADP
fcis-25096	109	12	the	the	DET
fcis-25096	109	13	same	same	ADJ
fcis-25096	109	14	grid	grid	NOUN
fcis-25096	109	15	map	map	NOUN
fcis-25096	109	16	.	.	PUNCT
fcis-25096	110	1	combining	combine	VERB
fcis-25096	110	2	figures	figure	NOUN
fcis-25096	110	3	4	4	NUM
fcis-25096	110	4	and	and	CCONJ
fcis-25096	110	5	5	5	NUM
fcis-25096	110	6	,	,	PUNCT
fcis-25096	110	7	it	it	PRON
fcis-25096	110	8	can	can	AUX
fcis-25096	110	9	be	be	AUX
fcis-25096	110	10	clearly	clearly	ADV
fcis-25096	110	11	observed	observe	VERB
fcis-25096	110	12	that	that	SCONJ
fcis-25096	110	13	using	use	VERB
fcis-25096	110	14	standard	standard	ADJ
fcis-25096	110	15	swarm	swarm	NOUN
fcis-25096	110	16	intelligence	intelligence	NOUN
fcis-25096	110	17	algorithms	algorithm	NOUN
fcis-25096	110	18	for	for	ADP
fcis-25096	110	19	path	path	NOUN
fcis-25096	110	20	planning	planning	NOUN
fcis-25096	110	21	is	be	AUX
fcis-25096	110	22	prone	prone	ADJ
fcis-25096	110	23	to	to	ADP
fcis-25096	110	24	getting	getting	AUX
fcis-25096	110	25	stuck	stick	VERB
fcis-25096	110	26	in	in	ADP
fcis-25096	110	27	local	local	ADJ
fcis-25096	110	28	optima	optima	NOUN
fcis-25096	110	29	,	,	PUNCT
fcis-25096	110	30	particularly	particularly	ADV
fcis-25096	110	31	evident	evident	ADJ
fcis-25096	110	32	in	in	ADP
fcis-25096	110	33	the	the	DET
fcis-25096	110	34	pso	pso	NOUN
fcis-25096	110	35	algorithm	algorithm	NOUN
fcis-25096	110	36	;	;	PUNCT
fcis-25096	110	37	the	the	DET
fcis-25096	110	38	simple	simple	ADJ
fcis-25096	110	39	fusion	fusion	NOUN
fcis-25096	110	40	of	of	ADP
fcis-25096	110	41	two	two	NUM
fcis-25096	110	42	intelligent	intelligent	ADJ
fcis-25096	110	43	algorithms	algorithm	NOUN
fcis-25096	110	44	for	for	ADP
fcis-25096	110	45	path	path	NOUN
fcis-25096	110	46	planning	planning	NOUN
fcis-25096	110	47	can	can	AUX
fcis-25096	110	48	improve	improve	VERB
fcis-25096	110	49	the	the	DET
fcis-25096	110	50	problem	problem	NOUN
fcis-25096	110	51	of	of	ADP
fcis-25096	110	52	weak	weak	ADJ
fcis-25096	110	53	local	local	ADJ
fcis-25096	110	54	search	search	NOUN
fcis-25096	110	55	,	,	PUNCT
fcis-25096	110	56	but	but	CCONJ
fcis-25096	110	57	the	the	DET
fcis-25096	110	58	search	search	NOUN
fcis-25096	110	59	takes	take	VERB
fcis-25096	110	60	a	a	DET
fcis-25096	110	61	long	long	ADJ
fcis-25096	110	62	time	time	NOUN
fcis-25096	110	63	,	,	PUNCT
fcis-25096	110	64	occupies	occupy	VERB
fcis-25096	110	65	a	a	DET
fcis-25096	110	66	large	large	ADJ
fcis-25096	110	67	amount	amount	NOUN
fcis-25096	110	68	of	of	ADP
fcis-25096	110	69	memory	memory	NOUN
fcis-25096	110	70	,	,	PUNCT
fcis-25096	110	71	and	and	CCONJ
fcis-25096	110	72	has	have	VERB
fcis-25096	110	73	weak	weak	ADJ
fcis-25096	110	74	real	real	ADJ
fcis-25096	110	75	-	-	PUNCT
fcis-25096	110	76	time	time	NOUN
fcis-25096	110	77	performance[13	performance[13	NOUN
fcis-25096	110	78	]	]	PUNCT
fcis-25096	110	79	.	.	PUNCT
fcis-25096	111	1	this	this	DET
fcis-25096	111	2	article	article	NOUN
fcis-25096	111	3	introduces	introduce	VERB
fcis-25096	111	4	multiple	multiple	ADJ
fcis-25096	111	5	optimization	optimization	NOUN
fcis-25096	111	6	strategies	strategy	NOUN
fcis-25096	111	7	to	to	PART
fcis-25096	111	8	optimize	optimize	VERB
fcis-25096	111	9	standard	standard	ADJ
fcis-25096	111	10	intelligent	intelligent	ADJ
fcis-25096	111	11	algorithms	algorithm	NOUN
fcis-25096	111	12	,	,	PUNCT
fcis-25096	111	13	aiming	aim	VERB
fcis-25096	111	14	to	to	PART
fcis-25096	111	15	minimize	minimize	VERB
fcis-25096	111	16	algorithm	algorithm	NOUN
fcis-25096	111	17	complexity	complexity	NOUN
fcis-25096	111	18	while	while	SCONJ
fcis-25096	111	19	maximizing	maximize	VERB
fcis-25096	111	20	search	search	NOUN
fcis-25096	111	21	accuracy	accuracy	NOUN
fcis-25096	111	22	and	and	CCONJ
fcis-25096	111	23	planning	plan	VERB
fcis-25096	111	24	the	the	DET
fcis-25096	111	25	shortest	short	ADJ
fcis-25096	111	26	path	path	NOUN
fcis-25096	111	27	that	that	PRON
fcis-25096	111	28	meets	meet	VERB
fcis-25096	111	29	practical	practical	ADJ
fcis-25096	111	30	needs	need	NOUN
fcis-25096	111	31	.	.	PUNCT
fcis-25096	111	32	table	table	NOUN
fcis-25096	111	33	2	2	NUM
fcis-25096	111	34	and	and	CCONJ
fcis-25096	111	35	figure	figure	VERB
fcis-25096	111	36	6	6	NUM
fcis-25096	111	37	show	show	NOUN
fcis-25096	111	38	experiments	experiment	NOUN
fcis-25096	111	39	conducted	conduct	VERB
fcis-25096	111	40	in	in	ADP
fcis-25096	111	41	the	the	DET
fcis-25096	111	42	environment	environment	NOUN
fcis-25096	111	43	of	of	ADP
fcis-25096	111	44	figure	figure	NOUN
fcis-25096	111	45	1	1	NUM
fcis-25096	111	46	(	(	PUNCT
fcis-25096	111	47	b	b	NOUN
fcis-25096	111	48	)	)	PUNCT
fcis-25096	111	49	,	,	PUNCT
fcis-25096	111	50	which	which	PRON
fcis-25096	111	51	is	be	AUX
fcis-25096	111	52	more	more	ADV
fcis-25096	111	53	complex	complex	ADJ
fcis-25096	111	54	and	and	CCONJ
fcis-25096	111	55	has	have	VERB
fcis-25096	111	56	more	more	ADJ
fcis-25096	111	57	obstacles	obstacle	NOUN
fcis-25096	111	58	.	.	PUNCT
fcis-25096	112	1	table	table	NOUN
fcis-25096	112	2	2	2	NUM
fcis-25096	112	3	.	.	PUNCT
fcis-25096	112	4	comparison	comparison	NOUN
fcis-25096	112	5	of	of	ADP
fcis-25096	112	6	path	path	NOUN
fcis-25096	112	7	planning	planning	NOUN
fcis-25096	112	8	results	result	NOUN
fcis-25096	112	9	algorithm	algorithm	PROPN
fcis-25096	112	10	shortest	short	ADJ
fcis-25096	112	11	path	path	NOUN
fcis-25096	112	12	/cm	/cm	PUNCT
fcis-25096	113	1	average	average	ADJ
fcis-25096	113	2	path	path	NOUN
fcis-25096	113	3	/cm	/cm	PUNCT
fcis-25096	114	1	average	average	ADJ
fcis-25096	114	2	time	time	NOUN
fcis-25096	114	3	/	/	SYM
fcis-25096	114	4	s	s	NOUN
fcis-25096	114	5	mpso	mpso	ADJ
fcis-25096	114	6	30.8743	30.8743	NUM
fcis-25096	114	7	31.629	31.629	NUM
fcis-25096	114	8	3.0223	3.0223	NUM
fcis-25096	114	9	pso	pso	NOUN
fcis-25096	114	10	37.1263	37.1263	NUM
fcis-25096	114	11	42.2978	42.2978	NUM
fcis-25096	114	12	0.68269	0.68269	NUM
fcis-25096	114	13	ga	ga	NOUN
fcis-25096	114	14	30.6143	30.6143	NOUN
fcis-25096	114	15	36.2148	36.2148	NUM
fcis-25096	114	16	3.5334	3.5334	NUM
fcis-25096	114	17	aco_ga	aco_ga	X
fcis-25096	114	18	29.4203	29.4203	NUM
fcis-25096	114	19	30.2661	30.2661	NUM
fcis-25096	114	20	3.1433	3.1433	NUM
fcis-25096	114	21	soa	soa	NOUN
fcis-25096	114	22	36.6224	36.6224	NUM
fcis-25096	114	23	42.476	42.476	NUM
fcis-25096	114	24	1.1332	1.1332	NUM
fcis-25096	114	25	fig	fig	NOUN
fcis-25096	114	26	6	6	NUM
fcis-25096	114	27	.	.	PUNCT
fcis-25096	114	28	multiple	multiple	ADJ
fcis-25096	114	29	algorithm	algorithm	NOUN
fcis-25096	114	30	path	path	NOUN
fcis-25096	114	31	planning	planning	NOUN
fcis-25096	114	32	results	result	NOUN
fcis-25096	114	33	for	for	ADP
fcis-25096	114	34	complex	complex	ADJ
fcis-25096	114	35	map	map	NOUN
fcis-25096	114	36	models	model	NOUN
fcis-25096	114	37	13	13	NUM
fcis-25096	114	38	when	when	SCONJ
fcis-25096	114	39	the	the	DET
fcis-25096	114	40	environmental	environmental	ADJ
fcis-25096	114	41	map	map	NOUN
fcis-25096	114	42	becomes	become	VERB
fcis-25096	114	43	more	more	ADV
fcis-25096	114	44	complex	complex	ADJ
fcis-25096	114	45	,	,	PUNCT
fcis-25096	114	46	the	the	DET
fcis-25096	114	47	advantages	advantage	NOUN
fcis-25096	114	48	of	of	ADP
fcis-25096	114	49	the	the	DET
fcis-25096	114	50	algorithm	algorithm	NOUN
fcis-25096	114	51	in	in	ADP
fcis-25096	114	52	this	this	DET
fcis-25096	114	53	article	article	NOUN
fcis-25096	114	54	can	can	AUX
fcis-25096	114	55	be	be	AUX
fcis-25096	114	56	better	well	ADV
fcis-25096	114	57	reflected	reflect	VERB
fcis-25096	114	58	.	.	PUNCT
fcis-25096	115	1	in	in	ADP
fcis-25096	115	2	table	table	NOUN
fcis-25096	115	3	2	2	NUM
fcis-25096	115	4	,	,	PUNCT
fcis-25096	115	5	the	the	DET
fcis-25096	115	6	performance	performance	NOUN
fcis-25096	115	7	of	of	ADP
fcis-25096	115	8	the	the	DET
fcis-25096	115	9	improved	improved	ADJ
fcis-25096	115	10	particle	particle	NOUN
fcis-25096	115	11	swarm	swarm	NOUN
fcis-25096	115	12	algorithm	algorithm	NOUN
fcis-25096	115	13	is	be	AUX
fcis-25096	115	14	significantly	significantly	ADV
fcis-25096	115	15	better	well	ADJ
fcis-25096	115	16	than	than	ADP
fcis-25096	115	17	other	other	ADJ
fcis-25096	115	18	algorithms	algorithm	NOUN
fcis-25096	115	19	,	,	PUNCT
fcis-25096	115	20	and	and	CCONJ
fcis-25096	115	21	the	the	DET
fcis-25096	115	22	performance	performance	NOUN
fcis-25096	115	23	of	of	ADP
fcis-25096	115	24	the	the	DET
fcis-25096	115	25	algorithm	algorithm	NOUN
fcis-25096	115	26	has	have	AUX
fcis-25096	115	27	been	be	AUX
fcis-25096	115	28	improved	improve	VERB
fcis-25096	115	29	.	.	PUNCT
fcis-25096	116	1	the	the	DET
fcis-25096	116	2	length	length	NOUN
fcis-25096	116	3	of	of	ADP
fcis-25096	116	4	the	the	DET
fcis-25096	116	5	optimal	optimal	ADJ
fcis-25096	116	6	path	path	NOUN
fcis-25096	116	7	planned	plan	VERB
fcis-25096	116	8	by	by	ADP
fcis-25096	116	9	the	the	DET
fcis-25096	116	10	mpso	mpso	ADJ
fcis-25096	116	11	algorithm	algorithm	NOUN
fcis-25096	116	12	is	be	AUX
fcis-25096	116	13	smaller	small	ADJ
fcis-25096	116	14	than	than	ADP
fcis-25096	116	15	the	the	DET
fcis-25096	116	16	path	path	NOUN
fcis-25096	116	17	length	length	NOUN
fcis-25096	116	18	planned	plan	VERB
fcis-25096	116	19	by	by	ADP
fcis-25096	116	20	the	the	DET
fcis-25096	116	21	basic	basic	ADJ
fcis-25096	116	22	pso	pso	NOUN
fcis-25096	116	23	algorithm	algorithm	NOUN
fcis-25096	116	24	,	,	PUNCT
fcis-25096	116	25	which	which	PRON
fcis-25096	116	26	solves	solve	VERB
fcis-25096	116	27	the	the	DET
fcis-25096	116	28	problem	problem	NOUN
fcis-25096	116	29	of	of	ADP
fcis-25096	116	30	pso	pso	NOUN
fcis-25096	116	31	algorithm	algorithm	NOUN
fcis-25096	116	32	easily	easily	ADV
fcis-25096	116	33	getting	getting	AUX
fcis-25096	116	34	stuck	stick	VERB
fcis-25096	116	35	in	in	ADP
fcis-25096	116	36	local	local	ADJ
fcis-25096	116	37	optima	optima	PROPN
fcis-25096	116	38	.	.	PUNCT
fcis-25096	117	1	figure	figure	NOUN
fcis-25096	117	2	6	6	NUM
fcis-25096	117	3	shows	show	VERB
fcis-25096	117	4	the	the	DET
fcis-25096	117	5	optimal	optimal	ADJ
fcis-25096	117	6	route	route	NOUN
fcis-25096	117	7	planned	plan	VERB
fcis-25096	117	8	in	in	ADP
fcis-25096	117	9	a	a	DET
fcis-25096	117	10	complex	complex	ADJ
fcis-25096	117	11	grid	grid	NOUN
fcis-25096	117	12	map	map	NOUN
fcis-25096	117	13	.	.	PUNCT
fcis-25096	118	1	it	it	PRON
fcis-25096	118	2	can	can	AUX
fcis-25096	118	3	be	be	AUX
fcis-25096	118	4	seen	see	VERB
fcis-25096	118	5	that	that	SCONJ
fcis-25096	118	6	the	the	DET
fcis-25096	118	7	improved	improved	ADJ
fcis-25096	118	8	algorithm	algorithm	NOUN
fcis-25096	118	9	in	in	ADP
fcis-25096	118	10	this	this	DET
fcis-25096	118	11	paper	paper	NOUN
fcis-25096	118	12	has	have	AUX
fcis-25096	118	13	always	always	ADV
fcis-25096	118	14	maintained	maintain	VERB
fcis-25096	118	15	excellent	excellent	ADJ
fcis-25096	118	16	planning	planning	NOUN
fcis-25096	118	17	path	path	NOUN
fcis-25096	118	18	capability	capability	NOUN
fcis-25096	118	19	and	and	CCONJ
fcis-25096	118	20	better	well	ADJ
fcis-25096	118	21	algorithm	algorithm	NOUN
fcis-25096	118	22	stability	stability	NOUN
fcis-25096	118	23	.	.	PUNCT
fcis-25096	119	1	5	5	X
fcis-25096	119	2	.	.	X
fcis-25096	119	3	conclusion	conclusion	NOUN
fcis-25096	119	4	the	the	DET
fcis-25096	119	5	mpso	mpso	ADJ
fcis-25096	119	6	algorithm	algorithm	NOUN
fcis-25096	119	7	proposed	propose	VERB
fcis-25096	119	8	in	in	ADP
fcis-25096	119	9	this	this	DET
fcis-25096	119	10	article	article	NOUN
fcis-25096	119	11	first	first	ADV
fcis-25096	119	12	initializes	initialize	VERB
fcis-25096	119	13	the	the	DET
fcis-25096	119	14	population	population	NOUN
fcis-25096	119	15	by	by	ADP
fcis-25096	119	16	introducing	introduce	VERB
fcis-25096	119	17	an	an	DET
fcis-25096	119	18	improved	improved	ADJ
fcis-25096	119	19	sine	sine	ADJ
fcis-25096	119	20	chaotic	chaotic	ADJ
fcis-25096	119	21	map	map	NOUN
fcis-25096	119	22	to	to	PART
fcis-25096	119	23	enhance	enhance	VERB
fcis-25096	119	24	its	its	PRON
fcis-25096	119	25	diversity	diversity	NOUN
fcis-25096	119	26	.	.	PUNCT
fcis-25096	120	1	then	then	ADV
fcis-25096	120	2	,	,	PUNCT
fcis-25096	120	3	the	the	DET
fcis-25096	120	4	quantum	quantum	NOUN
fcis-25096	120	5	concept	concept	NOUN
fcis-25096	120	6	is	be	AUX
fcis-25096	120	7	introduced	introduce	VERB
fcis-25096	120	8	to	to	PART
fcis-25096	120	9	improve	improve	VERB
fcis-25096	120	10	the	the	DET
fcis-25096	120	11	global	global	ADJ
fcis-25096	120	12	search	search	NOUN
fcis-25096	120	13	ability	ability	NOUN
fcis-25096	120	14	and	and	CCONJ
fcis-25096	120	15	convergence	convergence	NOUN
fcis-25096	120	16	of	of	ADP
fcis-25096	120	17	the	the	DET
fcis-25096	120	18	traditional	traditional	ADJ
fcis-25096	120	19	pso	pso	NOUN
fcis-25096	120	20	algorithm	algorithm	NOUN
fcis-25096	120	21	by	by	ADP
fcis-25096	120	22	adding	add	VERB
fcis-25096	120	23	two	two	NUM
fcis-25096	120	24	probability	probability	NOUN
fcis-25096	120	25	density	density	NOUN
fcis-25096	120	26	functions	function	NOUN
fcis-25096	120	27	.	.	PUNCT
fcis-25096	121	1	finally	finally	ADV
fcis-25096	121	2	,	,	PUNCT
fcis-25096	121	3	the	the	DET
fcis-25096	121	4	levy	levy	NOUN
fcis-25096	121	5	flight	flight	NOUN
fcis-25096	121	6	strategy	strategy	NOUN
fcis-25096	121	7	is	be	AUX
fcis-25096	121	8	introduced	introduce	VERB
fcis-25096	121	9	to	to	PART
fcis-25096	121	10	update	update	VERB
fcis-25096	121	11	the	the	DET
fcis-25096	121	12	global	global	ADJ
fcis-25096	121	13	optimal	optimal	ADJ
fcis-25096	121	14	particles	particle	NOUN
fcis-25096	121	15	,	,	PUNCT
fcis-25096	121	16	solving	solve	VERB
fcis-25096	121	17	the	the	DET
fcis-25096	121	18	evolution	evolution	NOUN
fcis-25096	121	19	problem	problem	NOUN
fcis-25096	121	20	of	of	ADP
fcis-25096	121	21	the	the	DET
fcis-25096	121	22	optimal	optimal	ADJ
fcis-25096	121	23	particles	particle	NOUN
fcis-25096	121	24	and	and	CCONJ
fcis-25096	121	25	enhancing	enhance	VERB
fcis-25096	121	26	the	the	DET
fcis-25096	121	27	convergence	convergence	NOUN
fcis-25096	121	28	accuracy	accuracy	NOUN
fcis-25096	121	29	of	of	ADP
fcis-25096	121	30	the	the	DET
fcis-25096	121	31	particle	particle	NOUN
fcis-25096	121	32	swarm	swarm	NOUN
fcis-25096	121	33	algorithm	algorithm	NOUN
fcis-25096	121	34	.	.	PUNCT
fcis-25096	122	1	the	the	DET
fcis-25096	122	2	algorithm	algorithm	NOUN
fcis-25096	122	3	proposed	propose	VERB
fcis-25096	122	4	in	in	ADP
fcis-25096	122	5	this	this	DET
fcis-25096	122	6	article	article	NOUN
fcis-25096	122	7	was	be	AUX
fcis-25096	122	8	applied	apply	VERB
fcis-25096	122	9	to	to	ADP
fcis-25096	122	10	robot	robot	NOUN
fcis-25096	122	11	path	path	NOUN
fcis-25096	122	12	planning	planning	NOUN
fcis-25096	122	13	in	in	ADP
fcis-25096	122	14	two	two	NUM
fcis-25096	122	15	different	different	ADJ
fcis-25096	122	16	grid	grid	NOUN
fcis-25096	122	17	maps	map	NOUN
fcis-25096	122	18	,	,	PUNCT
fcis-25096	122	19	namely	namely	ADV
fcis-25096	122	20	pso	pso	NOUN
fcis-25096	122	21	,	,	PUNCT
fcis-25096	122	22	aoc_ga	aoc_ga	PROPN
fcis-25096	122	23	,	,	PUNCT
fcis-25096	122	24	soa	soa	PROPN
fcis-25096	122	25	,	,	PUNCT
fcis-25096	122	26	and	and	CCONJ
fcis-25096	122	27	ga	ga	PROPN
fcis-25096	122	28	.	.	PUNCT
fcis-25096	123	1	the	the	DET
fcis-25096	123	2	experimental	experimental	ADJ
fcis-25096	123	3	results	result	NOUN
fcis-25096	123	4	showed	show	VERB
fcis-25096	123	5	that	that	SCONJ
fcis-25096	123	6	the	the	DET
fcis-25096	123	7	mpso	mpso	ADJ
fcis-25096	123	8	algorithm	algorithm	NOUN
fcis-25096	123	9	performed	perform	VERB
fcis-25096	123	10	well	well	ADV
fcis-25096	123	11	in	in	ADP
fcis-25096	123	12	shortest	short	ADJ
fcis-25096	123	13	path	path	NOUN
fcis-25096	123	14	,	,	PUNCT
fcis-25096	123	15	average	average	ADJ
fcis-25096	123	16	path	path	NOUN
fcis-25096	123	17	,	,	PUNCT
fcis-25096	123	18	and	and	CCONJ
fcis-25096	123	19	convergence	convergence	NOUN
fcis-25096	123	20	speed	speed	NOUN
fcis-25096	123	21	,	,	PUNCT
fcis-25096	123	22	outperforming	outperform	VERB
fcis-25096	123	23	a	a	DET
fcis-25096	123	24	single	single	ADJ
fcis-25096	123	25	intelligent	intelligent	ADJ
fcis-25096	123	26	algorithm	algorithm	NOUN
fcis-25096	123	27	in	in	ADP
fcis-25096	123	28	accuracy	accuracy	NOUN
fcis-25096	123	29	and	and	CCONJ
fcis-25096	123	30	slightly	slightly	ADV
fcis-25096	123	31	inferior	inferior	ADJ
fcis-25096	123	32	to	to	ADP
fcis-25096	123	33	the	the	DET
fcis-25096	123	34	fusion	fusion	NOUN
fcis-25096	123	35	of	of	ADP
fcis-25096	123	36	multiple	multiple	ADJ
fcis-25096	123	37	intelligent	intelligent	ADJ
fcis-25096	123	38	algorithms	algorithm	NOUN
fcis-25096	123	39	in	in	ADP
fcis-25096	123	40	searching	search	VERB
fcis-25096	123	41	for	for	ADP
fcis-25096	123	42	the	the	DET
fcis-25096	123	43	best	good	ADJ
fcis-25096	123	44	path	path	NOUN
fcis-25096	123	45	,	,	PUNCT
fcis-25096	123	46	demonstrating	demonstrate	VERB
fcis-25096	123	47	good	good	ADJ
fcis-25096	123	48	performance	performance	NOUN
fcis-25096	123	49	and	and	CCONJ
fcis-25096	123	50	providing	provide	VERB
fcis-25096	123	51	a	a	DET
fcis-25096	123	52	feasible	feasible	ADJ
fcis-25096	123	53	solution	solution	NOUN
fcis-25096	123	54	for	for	ADP
fcis-25096	123	55	mobile	mobile	ADJ
fcis-25096	123	56	robots	robot	NOUN
fcis-25096	123	57	to	to	PART
fcis-25096	123	58	plan	plan	VERB
fcis-25096	123	59	effective	effective	ADJ
fcis-25096	123	60	shortest	short	ADJ
fcis-25096	123	61	paths	path	NOUN
fcis-25096	123	62	.	.	PUNCT
fcis-25096	124	1	references	reference	NOUN
fcis-25096	124	2	[	[	X
fcis-25096	124	3	1	1	X
fcis-25096	124	4	]	]	X
fcis-25096	124	5	dai	dai	PROPN
fcis-25096	124	6	xiaolin	xiaolin	PROPN
fcis-25096	124	7	,	,	PUNCT
fcis-25096	124	8	shuai	shuai	PROPN
fcis-25096	124	9	long	long	PROPN
fcis-25096	124	10	,	,	PUNCT
fcis-25096	124	11	zhiwen	zhiwen	PROPN
fcis-25096	124	12	zhang	zhang	PROPN
fcis-25096	124	13	,	,	PUNCT
fcis-25096	124	14	et	et	PROPN
fcis-25096	124	15	al	al	PROPN
fcis-25096	124	16	.	.	PUNCT
fcis-25096	125	1	"	"	PUNCT
fcis-25096	125	2	mobile	mobile	ADJ
fcis-25096	125	3	robot	robot	NOUN
fcis-25096	125	4	path	path	NOUN
fcis-25096	125	5	planning	planning	NOUN
fcis-25096	125	6	based	base	VERB
fcis-25096	125	7	on	on	ADP
fcis-25096	125	8	ant	ant	ADJ
fcis-25096	125	9	colony	colony	NOUN
fcis-25096	125	10	algorithm	algorithm	NOUN
fcis-25096	125	11	with	with	ADP
fcis-25096	125	12	a	a	DET
fcis-25096	125	13	*	*	X
fcis-25096	125	14	heuristic	heuristic	ADJ
fcis-25096	125	15	method[j	method[j	NOUN
fcis-25096	125	16	]	]	PUNCT
fcis-25096	125	17	.	.	PUNCT
fcis-25096	125	18	"	"	PUNCT
fcis-25096	126	1	frontiers	frontier	NOUN
fcis-25096	126	2	in	in	ADP
fcis-25096	126	3	neurorobotics,2019,16(4):13	neurorobotics,2019,16(4):13	NOUN
fcis-25096	126	4	-	-	SYM
fcis-25096	126	5	15	15	NUM
fcis-25096	126	6	.	.	PUNCT
fcis-25096	127	1	[	[	X
fcis-25096	127	2	2	2	NUM
fcis-25096	127	3	]	]	X
fcis-25096	127	4	kennedy	kennedy	PROPN
fcis-25096	127	5	,	,	PUNCT
fcis-25096	127	6	j.	j.	PROPN
fcis-25096	127	7	,	,	PUNCT
fcis-25096	127	8	&	&	CCONJ
fcis-25096	127	9	eberhart	eberhart	PROPN
fcis-25096	127	10	,	,	PUNCT
fcis-25096	127	11	r.	r.	PROPN
fcis-25096	127	12	(	(	PUNCT
fcis-25096	127	13	1995	1995	NUM
fcis-25096	127	14	,	,	PUNCT
fcis-25096	127	15	november	november	PROPN
fcis-25096	127	16	)	)	PUNCT
fcis-25096	127	17	.	.	PUNCT
fcis-25096	128	1	particle	particle	NOUN
fcis-25096	128	2	swarm	swarm	NOUN
fcis-25096	128	3	optimization	optimization	NOUN
fcis-25096	128	4	.	.	PUNCT
fcis-25096	129	1	in	in	ADP
fcis-25096	129	2	proceedings	proceeding	NOUN
fcis-25096	129	3	of	of	ADP
fcis-25096	129	4	icnn'95	icnn'95	PROPN
fcis-25096	129	5	-	-	PUNCT
fcis-25096	129	6	international	international	ADJ
fcis-25096	129	7	conference	conference	NOUN
fcis-25096	129	8	on	on	ADP
fcis-25096	129	9	neural	neural	ADJ
fcis-25096	129	10	networks	network	NOUN
fcis-25096	129	11	(	(	PUNCT
fcis-25096	129	12	vol	vol	NOUN
fcis-25096	129	13	.	.	NOUN
fcis-25096	129	14	4	4	NUM
fcis-25096	129	15	,	,	PUNCT
fcis-25096	129	16	pp	pp	ADJ
fcis-25096	129	17	.	.	PUNCT
fcis-25096	129	18	1942	1942	NUM
fcis-25096	129	19	-	-	SYM
fcis-25096	129	20	1948	1948	NUM
fcis-25096	129	21	)	)	PUNCT
fcis-25096	129	22	.	.	PUNCT
fcis-25096	130	1	ieee	ieee	NOUN
fcis-25096	130	2	.	.	PUNCT
fcis-25096	131	1	[	[	X
fcis-25096	131	2	3	3	NUM
fcis-25096	131	3	]	]	X
fcis-25096	131	4	feng	feng	X
fcis-25096	131	5	,	,	PUNCT
fcis-25096	131	6	y.	y.	PROPN
fcis-25096	131	7	,	,	PUNCT
fcis-25096	131	8	teng	teng	PROPN
fcis-25096	131	9	,	,	PUNCT
fcis-25096	131	10	g.	g.	PROPN
fcis-25096	131	11	f.	f.	PROPN
fcis-25096	131	12	,	,	PUNCT
fcis-25096	131	13	wang	wang	PROPN
fcis-25096	131	14	,	,	PUNCT
fcis-25096	131	15	a.	a.	NOUN
fcis-25096	131	16	x.	x.	PROPN
fcis-25096	131	17	,	,	PUNCT
fcis-25096	131	18	&	&	CCONJ
fcis-25096	131	19	yao	yao	PROPN
fcis-25096	131	20	,	,	PUNCT
fcis-25096	131	21	y.	y.	PROPN
fcis-25096	131	22	m.	m.	PROPN
fcis-25096	131	23	(	(	PUNCT
fcis-25096	131	24	2007	2007	NUM
fcis-25096	131	25	,	,	PUNCT
fcis-25096	131	26	august	august	PROPN
fcis-25096	131	27	)	)	PUNCT
fcis-25096	131	28	.	.	PUNCT
fcis-25096	132	1	chaotic	chaotic	ADJ
fcis-25096	132	2	inertia	inertia	NOUN
fcis-25096	132	3	weight	weight	NOUN
fcis-25096	132	4	in	in	ADP
fcis-25096	132	5	particle	particle	NOUN
fcis-25096	132	6	swarm	swarm	NOUN
fcis-25096	132	7	optimization	optimization	NOUN
fcis-25096	132	8	.	.	PUNCT
fcis-25096	133	1	in	in	ADP
fcis-25096	133	2	second	second	ADJ
fcis-25096	133	3	international	international	ADJ
fcis-25096	133	4	conference	conference	NOUN
fcis-25096	133	5	on	on	ADP
fcis-25096	133	6	innovative	innovative	ADJ
fcis-25096	133	7	computing	computing	NOUN
fcis-25096	133	8	,	,	PUNCT
fcis-25096	133	9	informatio	informatio	NOUN
fcis-25096	133	10	and	and	CCONJ
fcis-25096	133	11	control	control	NOUN
fcis-25096	133	12	(	(	PUNCT
fcis-25096	133	13	icicic	icicic	NOUN
fcis-25096	133	14	2007	2007	NUM
fcis-25096	133	15	)	)	PUNCT
fcis-25096	133	16	(	(	PUNCT
fcis-25096	133	17	pp	pp	X
fcis-25096	133	18	.	.	PUNCT
fcis-25096	133	19	475	475	NUM
fcis-25096	133	20	-	-	SYM
fcis-25096	133	21	475	475	NUM
fcis-25096	133	22	)	)	PUNCT
fcis-25096	133	23	.	.	PUNCT
fcis-25096	134	1	ieee	ieee	NOUN
fcis-25096	134	2	.	.	PUNCT
fcis-25096	135	1	[	[	X
fcis-25096	135	2	4	4	NUM
fcis-25096	135	3	]	]	X
fcis-25096	135	4	tian	tian	PROPN
fcis-25096	135	5	,	,	PUNCT
fcis-25096	135	6	d.	d.	PROPN
fcis-25096	135	7	,	,	PUNCT
fcis-25096	135	8	zhao	zhao	PROPN
fcis-25096	135	9	,	,	PUNCT
fcis-25096	135	10	x.	x.	PROPN
fcis-25096	135	11	,	,	PUNCT
fcis-25096	135	12	&	&	CCONJ
fcis-25096	135	13	shi	shi	PROPN
fcis-25096	135	14	,	,	PUNCT
fcis-25096	135	15	y.	y.	PROPN
fcis-25096	135	16	(	(	PUNCT
fcis-25096	135	17	2019	2019	NUM
fcis-25096	135	18	)	)	PUNCT
fcis-25096	135	19	.	.	PUNCT
fcis-25096	136	1	chaotic	chaotic	ADJ
fcis-25096	136	2	particle	particle	NOUN
fcis-25096	136	3	swarm	swarm	NOUN
fcis-25096	136	4	optimization	optimization	NOUN
fcis-25096	136	5	with	with	ADP
fcis-25096	136	6	sigmoid	sigmoid	NOUN
fcis-25096	136	7	-	-	PUNCT
fcis-25096	136	8	based	base	VERB
fcis-25096	136	9	acceleration	acceleration	NOUN
fcis-25096	136	10	coefficients	coefficient	NOUN
fcis-25096	136	11	for	for	ADP
fcis-25096	136	12	numerical	numerical	ADJ
fcis-25096	136	13	function	function	NOUN
fcis-25096	136	14	optimization	optimization	NOUN
fcis-25096	136	15	.	.	PUNCT
fcis-25096	137	1	swarm	swarm	NOUN
fcis-25096	137	2	and	and	CCONJ
fcis-25096	137	3	evolutionary	evolutionary	ADJ
fcis-25096	137	4	computation	computation	NOUN
fcis-25096	137	5	,	,	PUNCT
fcis-25096	137	6	51	51	NUM
fcis-25096	137	7	,	,	PUNCT
fcis-25096	137	8	100552	100552	NUM
fcis-25096	137	9	.	.	PUNCT
fcis-25096	138	1	[	[	X
fcis-25096	138	2	5	5	X
fcis-25096	138	3	]	]	X
fcis-25096	138	4	yuanzhou	yuanzhou	PROPN
fcis-25096	138	5	z	z	PROPN
fcis-25096	138	6	,	,	PUNCT
fcis-25096	138	7	lei	lei	PROPN
fcis-25096	138	8	l	l	NOUN
fcis-25096	138	9	,	,	PUNCT
fcis-25096	138	10	long	long	ADJ
fcis-25096	138	11	q	q	NOUN
fcis-25096	138	12	,	,	PUNCT
fcis-25096	138	13	et	et	NOUN
fcis-25096	138	14	al.sine	al.sine	ADJ
fcis-25096	138	15	-	-	PUNCT
fcis-25096	138	16	ssa	ssa	ADJ
fcis-25096	138	17	-	-	PUNCT
fcis-25096	138	18	bp	bp	PROPN
fcis-25096	138	19	ship	ship	NOUN
fcis-25096	138	20	trajectory	trajectory	NOUN
fcis-25096	138	21	prediction	prediction	NOUN
fcis-25096	138	22	based	base	VERB
fcis-25096	138	23	on	on	ADP
fcis-25096	138	24	chaotic	chaotic	ADJ
fcis-25096	138	25	mapping	mapping	NOUN
fcis-25096	138	26	improved	improve	VERB
fcis-25096	138	27	sparrow	sparrow	NOUN
fcis-25096	138	28	search	search	NOUN
fcis-25096	138	29	algorithm[j].sensors,2023,23(2):704	algorithm[j].sensors,2023,23(2):704	NOUN
fcis-25096	138	30	-	-	NOUN
fcis-25096	138	31	704	704	NUM
fcis-25096	138	32	.	.	PUNCT
fcis-25096	139	1	[	[	X
fcis-25096	139	2	6	6	NUM
fcis-25096	139	3	]	]	X
fcis-25096	139	4	zhang	zhang	PROPN
fcis-25096	139	5	,	,	PUNCT
fcis-25096	139	6	j.	j.	PROPN
fcis-25096	139	7	,	,	PUNCT
fcis-25096	139	8	xiao	xiao	PROPN
fcis-25096	139	9	,	,	PUNCT
fcis-25096	139	10	m.	m.	NOUN
fcis-25096	139	11	,	,	PUNCT
fcis-25096	139	12	gao	gao	PROPN
fcis-25096	139	13	,	,	PUNCT
fcis-25096	139	14	l.	l.	PROPN
fcis-25096	139	15	,	,	PUNCT
fcis-25096	139	16	&	&	CCONJ
fcis-25096	139	17	pan	pan	PROPN
fcis-25096	139	18	,	,	PUNCT
fcis-25096	139	19	q.	q.	PROPN
fcis-25096	139	20	(	(	PUNCT
fcis-25096	139	21	2019	2019	NUM
fcis-25096	139	22	)	)	PUNCT
fcis-25096	139	23	.	.	PUNCT
fcis-25096	140	1	queuing	queue	VERB
fcis-25096	140	2	search	search	NOUN
fcis-25096	140	3	algorithm	algorithm	NOUN
fcis-25096	140	4	:	:	PUNCT
fcis-25096	140	5	a	a	DET
fcis-25096	140	6	novel	novel	ADJ
fcis-25096	140	7	metaheuristic	metaheuristic	ADJ
fcis-25096	140	8	algorithm	algorithm	NOUN
fcis-25096	140	9	for	for	ADP
fcis-25096	140	10	solving	solve	VERB
fcis-25096	140	11	engineering	engineering	NOUN
fcis-25096	140	12	optimization	optimization	NOUN
fcis-25096	140	13	problems	problem	NOUN
fcis-25096	140	14	.	.	PUNCT
fcis-25096	141	1	applied	apply	VERB
fcis-25096	141	2	mathematical	mathematical	ADJ
fcis-25096	141	3	modelling	modelling	NOUN
fcis-25096	141	4	,	,	PUNCT
fcis-25096	141	5	63	63	NUM
fcis-25096	141	6	,	,	PUNCT
fcis-25096	141	7	464	464	NUM
fcis-25096	141	8	-	-	SYM
fcis-25096	141	9	490	490	NUM
fcis-25096	141	10	.	.	PUNCT
fcis-25096	142	1	[	[	X
fcis-25096	142	2	7	7	NUM
fcis-25096	142	3	]	]	X
fcis-25096	142	4	kamaruzaman	kamaruzaman	NOUN
fcis-25096	142	5	,	,	PUNCT
fcis-25096	142	6	anis	anis	PROPN
fcis-25096	142	7	farhan	farhan	PROPN
fcis-25096	142	8	,	,	PUNCT
fcis-25096	142	9	et	et	PROPN
fcis-25096	142	10	al	al	PROPN
fcis-25096	142	11	.	.	PUNCT
fcis-25096	143	1	"	"	PUNCT
fcis-25096	143	2	levy	levy	VERB
fcis-25096	143	3	flight	flight	NOUN
fcis-25096	143	4	algorithm	algorithm	NOUN
fcis-25096	143	5	for	for	ADP
fcis-25096	143	6	optimization	optimization	NOUN
fcis-25096	143	7	problems	problem	NOUN
fcis-25096	143	8	-	-	PUNCT
fcis-25096	143	9	a	a	DET
fcis-25096	143	10	literature	literature	NOUN
fcis-25096	143	11	review	review	NOUN
fcis-25096	143	12	.	.	PUNCT
fcis-25096	143	13	"	"	PUNCT
fcis-25096	144	1	applied	apply	VERB
fcis-25096	144	2	mechanics	mechanic	NOUN
fcis-25096	144	3	and	and	CCONJ
fcis-25096	144	4	materials	material	NOUN
fcis-25096	144	5	421	421	NUM
fcis-25096	144	6	(	(	PUNCT
fcis-25096	144	7	2013	2013	NUM
fcis-25096	144	8	):	):	PUNCT
fcis-25096	144	9	496	496	NUM
fcis-25096	144	10	-	-	SYM
fcis-25096	144	11	501	501	NUM
fcis-25096	144	12	.	.	PUNCT
fcis-25096	145	1	[	[	X
fcis-25096	145	2	8	8	NUM
fcis-25096	145	3	]	]	X
fcis-25096	145	4	gao	gao	PROPN
fcis-25096	145	5	,	,	PUNCT
fcis-25096	145	6	fang	fang	PROPN
fcis-25096	145	7	,	,	PUNCT
fcis-25096	145	8	qiang	qiang	PROPN
fcis-25096	145	9	zhao	zhao	PROPN
fcis-25096	145	10	,	,	PUNCT
fcis-25096	145	11	and	and	CCONJ
fcis-25096	145	12	guixian	guixian	PROPN
fcis-25096	145	13	li	li	PROPN
fcis-25096	145	14	.	.	PUNCT
fcis-25096	146	1	"	"	PUNCT
fcis-25096	146	2	modelling	modelling	NOUN
fcis-25096	146	3	and	and	CCONJ
fcis-25096	146	4	control	control	NOUN
fcis-25096	146	5	of	of	ADP
fcis-25096	146	6	vehicle	vehicle	NOUN
fcis-25096	146	7	magnetorheological	magnetorheological	ADJ
fcis-25096	146	8	seats	seat	NOUN
fcis-25096	146	9	based	base	VERB
fcis-25096	146	10	on	on	ADP
fcis-25096	146	11	bond	bond	NOUN
fcis-25096	146	12	graph	graph	NOUN
fcis-25096	146	13	.	.	PUNCT
fcis-25096	146	14	"	"	PUNCT
fcis-25096	147	1	china	china	PROPN
fcis-25096	147	2	sciencepaper	sciencepaper	PROPN
fcis-25096	147	3	/	/	SYM
fcis-25096	147	4	zhongguo	zhongguo	PROPN
fcis-25096	147	5	keji	keji	PROPN
fcis-25096	147	6	lunwen	lunwen	PROPN
fcis-25096	147	7	7.11	7.11	NUM
fcis-25096	147	8	(	(	PUNCT
fcis-25096	147	9	2012	2012	NUM
fcis-25096	147	10	):	):	PUNCT
fcis-25096	147	11	818	818	NUM
fcis-25096	147	12	-	-	SYM
fcis-25096	147	13	821	821	NUM
fcis-25096	147	14	.	.	PUNCT
fcis-25096	148	1	[	[	X
fcis-25096	148	2	9	9	NUM
fcis-25096	148	3	]	]	PUNCT
fcis-25096	148	4	dogar	dogar	NOUN
fcis-25096	148	5	f	f	PROPN
fcis-25096	148	6	r	r	PROPN
fcis-25096	148	7	,	,	PUNCT
fcis-25096	148	8	karagiannis	karagiannis	PROPN
fcis-25096	148	9	t	t	PROPN
fcis-25096	148	10	,	,	PUNCT
fcis-25096	148	11	ballani	ballani	PROPN
fcis-25096	148	12	h	h	NOUN
fcis-25096	148	13	,	,	PUNCT
fcis-25096	148	14	et	et	PROPN
fcis-25096	148	15	al	al	PROPN
fcis-25096	148	16	.	.	PROPN
fcis-25096	148	17	decentralized	decentralize	VERB
fcis-25096	148	18	taskaware	taskaware	NOUN
fcis-25096	148	19	scheduling	scheduling	NOUN
fcis-25096	148	20	for	for	ADP
fcis-25096	148	21	data	datum	NOUN
fcis-25096	148	22	center	center	PROPN
fcis-25096	148	23	networks[j	networks[j	PROPN
fcis-25096	148	24	]	]	PUNCT
fcis-25096	148	25	.	.	PUNCT
fcis-25096	148	26	acm	acm	PROPN
fcis-25096	148	27	sigcomm	sigcomm	PROPN
fcis-25096	148	28	computer	computer	NOUN
fcis-25096	148	29	communication	communication	NOUN
fcis-25096	148	30	review	review	NOUN
fcis-25096	148	31	2014,44(4	2014,44(4	NOUN
fcis-25096	148	32	):	):	PUNCT
fcis-25096	148	33	431	431	NUM
fcis-25096	148	34	-	-	SYM
fcis-25096	148	35	442	442	NUM
fcis-25096	148	36	.	.	PUNCT
fcis-25096	149	1	[	[	X
fcis-25096	149	2	10	10	NUM
fcis-25096	149	3	]	]	X
fcis-25096	149	4	wang	wang	PROPN
fcis-25096	149	5	l	l	PROPN
fcis-25096	149	6	,	,	PUNCT
fcis-25096	149	7	wang	wang	PROPN
fcis-25096	149	8	w.	w.	PROPN
fcis-25096	149	9	fair	fair	PROPN
fcis-25096	149	10	coflow	coflow	PROPN
fcis-25096	149	11	scheduling	scheduling	NOUN
fcis-25096	149	12	without	without	ADP
fcis-25096	149	13	prior	prior	ADJ
fcis-25096	149	14	knowledge[c	knowledge[c	NOUN
fcis-25096	149	15	]	]	PUNCT
fcis-25096	149	16	.	.	PUNCT
fcis-25096	149	17	2018	2018	NUM
fcis-25096	149	18	ieee	ieee	NOUN
fcis-25096	149	19	38th	38th	ADJ
fcis-25096	149	20	international	international	ADJ
fcis-25096	149	21	conference	conference	NOUN
fcis-25096	149	22	on	on	ADP
fcis-25096	149	23	distributed	distribute	VERB
fcis-25096	149	24	computing	computing	NOUN
fcis-25096	149	25	systems	system	NOUN
fcis-25096	149	26	(	(	PUNCT
fcis-25096	149	27	icdcs	icdcs	NOUN
fcis-25096	149	28	)	)	PUNCT
fcis-25096	149	29	,	,	PUNCT
fcis-25096	149	30	2018:22	2018:22	NUM
fcis-25096	149	31	-	-	SYM
fcis-25096	149	32	32	32	NUM
fcis-25096	149	33	.	.	PUNCT
fcis-25096	150	1	[	[	X
fcis-25096	150	2	11	11	NUM
fcis-25096	150	3	]	]	X
fcis-25096	150	4	qiu	qiu	PROPN
fcis-25096	150	5	z	z	PROPN
fcis-25096	150	6	,	,	PUNCT
fcis-25096	150	7	stein	stein	PROPN
fcis-25096	150	8	c	c	PROPN
fcis-25096	150	9	,	,	PUNCT
fcis-25096	150	10	zhong	zhong	PROPN
fcis-25096	150	11	y.	y.	PROPN
fcis-25096	150	12	minimizing	minimize	VERB
fcis-25096	150	13	the	the	DET
fcis-25096	150	14	total	total	NOUN
fcis-25096	150	15	weighted	weighted	ADJ
fcis-25096	150	16	completion	completion	NOUN
fcis-25096	150	17	time	time	NOUN
fcis-25096	150	18	of	of	ADP
fcis-25096	150	19	coflows	coflow	NOUN
fcis-25096	150	20	in	in	ADP
fcis-25096	150	21	datacenter	datacenter	NOUN
fcis-25096	150	22	networks[c	networks[c	PROPN
fcis-25096	150	23	]	]	PUNCT
fcis-25096	150	24	//	//	SYM
fcis-25096	150	25	27th	27th	PROPN
fcis-25096	150	26	acm	acm	PROPN
fcis-25096	150	27	symposium	symposium	NOUN
fcis-25096	150	28	on	on	ADP
fcis-25096	150	29	parallelism	parallelism	NOUN
fcis-25096	150	30	in	in	ADP
fcis-25096	150	31	algorithms	algorithm	NOUN
fcis-25096	150	32	and	and	CCONJ
fcis-25096	150	33	architectures	architecture	NOUN
fcis-25096	150	34	,	,	PUNCT
fcis-25096	150	35	2015:294	2015:294	NUM
fcis-25096	150	36	-	-	SYM
fcis-25096	150	37	303	303	NUM
fcis-25096	150	38	.	.	PUNCT
fcis-25096	151	1	[	[	X
fcis-25096	151	2	12	12	NUM
fcis-25096	151	3	]	]	X
fcis-25096	151	4	xin	xin	PROPN
fcis-25096	151	5	-	-	PROPN
fcis-25096	151	6	she	she	PRON
fcis-25096	151	7	yang	yang	PROPN
fcis-25096	151	8	,	,	PUNCT
fcis-25096	151	9	suash	suash	VERB
fcis-25096	151	10	deb	deb	PROPN
fcis-25096	151	11	.	.	PUNCT
fcis-25096	152	1	multiobjective	multiobjective	ADJ
fcis-25096	152	2	cuckoo	cuckoo	NOUN
fcis-25096	152	3	search	search	NOUN
fcis-25096	152	4	for	for	ADP
fcis-25096	152	5	design	design	NOUN
fcis-25096	152	6	optimization[j	optimization[j	NOUN
fcis-25096	152	7	]	]	PUNCT
fcis-25096	152	8	.	.	PUNCT
fcis-25096	153	1	computers	computer	NOUN
fcis-25096	153	2	and	and	CCONJ
fcis-25096	153	3	operations	operation	NOUN
fcis-25096	153	4	research	research	NOUN
fcis-25096	153	5	,	,	PUNCT
fcis-25096	153	6	2013,40(6	2013,40(6	PROPN
fcis-25096	153	7	)	)	PUNCT
fcis-25096	153	8	.	.	PUNCT
fcis-25096	154	1	[	[	X
fcis-25096	154	2	13	13	NUM
fcis-25096	154	3	]	]	X
fcis-25096	154	4	hüseyin	hüseyin	X
fcis-25096	154	5	hakli	hakli	NOUN
fcis-25096	154	6	,	,	PUNCT
fcis-25096	154	7	,	,	PUNCT
fcis-25096	154	8	and	and	CCONJ
fcis-25096	154	9	harun	harun	PROPN
fcis-25096	154	10	uğuz	uğuz	ADJ
fcis-25096	154	11	.	.	PUNCT
fcis-25096	155	1	"	"	PUNCT
fcis-25096	155	2	a	a	DET
fcis-25096	155	3	novel	novel	ADJ
fcis-25096	155	4	particle	particle	NOUN
fcis-25096	155	5	swarm	swarm	NOUN
fcis-25096	155	6	optimization	optimization	NOUN
fcis-25096	155	7	algorithm	algorithm	NOUN
fcis-25096	155	8	with	with	ADP
fcis-25096	155	9	levy	levy	NOUN
fcis-25096	155	10	flight	flight	NOUN
fcis-25096	155	11	.	.	PUNCT
fcis-25096	155	12	"	"	PUNCT
fcis-25096	155	13	applied	apply	VERB
fcis-25096	155	14	soft	soft	ADJ
fcis-25096	155	15	computing	compute	VERB
fcis-25096	155	16	23	23	NUM
fcis-25096	155	17	(	(	PUNCT
fcis-25096	155	18	2014	2014	NUM
fcis-25096	155	19	):	):	PUNCT
fcis-25096	155	20	333	333	NUM
fcis-25096	155	21	-	-	SYM
fcis-25096	155	22	345	345	NUM
fcis-25096	155	23	.	.	PUNCT